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Submission: On November 01 via api from HK — Scanned from DE
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<option value="acoustics"> Acoustics </option>
<option value="amh"> Acta Microbiologica Hellenica (AMH) </option>
<option value="actuators"> Actuators </option>
<option value="admsci"> Administrative Sciences </option>
<option value="adolescents"> Adolescents </option>
<option value="arm"> Advances in Respiratory Medicine (ARM) </option>
<option value="aerobiology"> Aerobiology </option>
<option value="aerospace"> Aerospace </option>
<option value="agriculture"> Agriculture </option>
<option value="agriengineering"> AgriEngineering </option>
<option value="agrochemicals"> Agrochemicals </option>
<option value="agronomy"> Agronomy </option>
<option value="ai"> AI </option>
<option value="air"> Air </option>
<option value="algorithms"> Algorithms </option>
<option value="allergies"> Allergies </option>
<option value="alloys"> Alloys </option>
<option value="analytica"> Analytica </option>
<option value="analytics"> Analytics </option>
<option value="anatomia"> Anatomia </option>
<option value="anesthres"> Anesthesia Research </option>
<option value="animals"> Animals </option>
<option value="antibiotics"> Antibiotics </option>
<option value="antibodies"> Antibodies </option>
<option value="antioxidants"> Antioxidants </option>
<option value="applbiosci"> Applied Biosciences </option>
<option value="applmech"> Applied Mechanics </option>
<option value="applmicrobiol"> Applied Microbiology </option>
<option value="applnano"> Applied Nano </option>
<option value="applsci"> Applied Sciences </option>
<option value="asi"> Applied System Innovation (ASI) </option>
<option value="appliedchem"> AppliedChem </option>
<option value="appliedmath"> AppliedMath </option>
<option value="aquacj"> Aquaculture Journal </option>
<option value="architecture"> Architecture </option>
<option value="arthropoda"> Arthropoda </option>
<option value="arts"> Arts </option>
<option value="astronomy"> Astronomy </option>
<option value="atmosphere"> Atmosphere </option>
<option value="atoms"> Atoms </option>
<option value="audiolres"> Audiology Research </option>
<option value="automation"> Automation </option>
<option value="axioms"> Axioms </option>
<option value="bacteria"> Bacteria </option>
<option value="batteries"> Batteries </option>
<option value="behavsci"> Behavioral Sciences </option>
<option value="beverages"> Beverages </option>
<option value="BDCC"> Big Data and Cognitive Computing (BDCC) </option>
<option value="biochem"> BioChem </option>
<option value="bioengineering" selected="selected"> Bioengineering </option>
<option value="biologics"> Biologics </option>
<option value="biology"> Biology </option>
<option value="blsf"> Biology and Life Sciences Forum </option>
<option value="biomass"> Biomass </option>
<option value="biomechanics"> Biomechanics </option>
<option value="biomed"> BioMed </option>
<option value="biomedicines"> Biomedicines </option>
<option value="biomedinformatics"> BioMedInformatics </option>
<option value="biomimetics"> Biomimetics </option>
<option value="biomolecules"> Biomolecules </option>
<option value="biophysica"> Biophysica </option>
<option value="biosensors"> Biosensors </option>
<option value="biotech"> BioTech </option>
<option value="birds"> Birds </option>
<option value="blockchains"> Blockchains </option>
<option value="brainsci"> Brain Sciences </option>
<option value="buildings"> Buildings </option>
<option value="businesses"> Businesses </option>
<option value="carbon"> C </option>
<option value="cancers"> Cancers </option>
<option value="cardiogenetics"> Cardiogenetics </option>
<option value="catalysts"> Catalysts </option>
<option value="cells"> Cells </option>
<option value="ceramics"> Ceramics </option>
<option value="challenges"> Challenges </option>
<option value="ChemEngineering"> ChemEngineering </option>
<option value="chemistry"> Chemistry </option>
<option value="chemproc"> Chemistry Proceedings </option>
<option value="chemosensors"> Chemosensors </option>
<option value="children"> Children </option>
<option value="chips"> Chips </option>
<option value="civileng"> CivilEng </option>
<option value="cleantechnol"> Clean Technologies (Clean Technol.) </option>
<option value="climate"> Climate </option>
<option value="ctn"> Clinical and Translational Neuroscience (CTN) </option>
<option value="clinpract"> Clinics and Practice </option>
<option value="clockssleep"> Clocks & Sleep </option>
<option value="coasts"> Coasts </option>
<option value="coatings"> Coatings </option>
<option value="colloids"> Colloids and Interfaces </option>
<option value="colorants"> Colorants </option>
<option value="commodities"> Commodities </option>
<option value="complications"> Complications </option>
<option value="compounds"> Compounds </option>
<option value="computation"> Computation </option>
<option value="csmf"> Computer Sciences & Mathematics Forum </option>
<option value="computers"> Computers </option>
<option value="condensedmatter"> Condensed Matter </option>
<option value="conservation"> Conservation </option>
<option value="constrmater"> Construction Materials </option>
<option value="cmd"> Corrosion and Materials Degradation (CMD) </option>
<option value="cosmetics"> Cosmetics </option>
<option value="covid"> COVID </option>
<option value="crops"> Crops </option>
<option value="cryo"> Cryo </option>
<option value="cryptography"> Cryptography </option>
<option value="crystals"> Crystals </option>
<option value="cimb"> Current Issues in Molecular Biology (CIMB) </option>
<option value="curroncol"> Current Oncology </option>
<option value="dairy"> Dairy </option>
<option value="data"> Data </option>
<option value="dentistry"> Dentistry Journal </option>
<option value="dermato"> Dermato </option>
<option value="dermatopathology"> Dermatopathology </option>
<option value="designs"> Designs </option>
<option value="diabetology"> Diabetology </option>
<option value="diagnostics"> Diagnostics </option>
<option value="dietetics"> Dietetics </option>
<option value="digital"> Digital </option>
<option value="disabilities"> Disabilities </option>
<option value="diseases"> Diseases </option>
<option value="diversity"> Diversity </option>
<option value="dna"> DNA </option>
<option value="drones"> Drones </option>
<option value="ddc"> Drugs and Drug Candidates (DDC) </option>
<option value="dynamics"> Dynamics </option>
<option value="earth"> Earth </option>
<option value="ecologies"> Ecologies </option>
<option value="econometrics"> Econometrics </option>
<option value="economies"> Economies </option>
<option value="education"> Education Sciences </option>
<option value="electricity"> Electricity </option>
<option value="electrochem"> Electrochem </option>
<option value="electronicmat"> Electronic Materials </option>
<option value="electronics"> Electronics </option>
<option value="ecm"> Emergency Care and Medicine </option>
<option value="encyclopedia"> Encyclopedia </option>
<option value="endocrines"> Endocrines </option>
<option value="energies"> Energies </option>
<option value="esa"> Energy Storage and Applications (ESA) </option>
<option value="eng"> Eng </option>
<option value="engproc"> Engineering Proceedings </option>
<option value="entropy"> Entropy </option>
<option value="environsciproc"> Environmental Sciences Proceedings </option>
<option value="environments"> Environments </option>
<option value="epidemiologia"> Epidemiologia </option>
<option value="epigenomes"> Epigenomes </option>
<option value="ebj"> European Burn Journal (EBJ) </option>
<option value="ejihpe"> European Journal of Investigation in Health, Psychology and Education (EJIHPE) </option>
<option value="fermentation"> Fermentation </option>
<option value="fibers"> Fibers </option>
<option value="fintech"> FinTech </option>
<option value="fire"> Fire </option>
<option value="fishes"> Fishes </option>
<option value="fluids"> Fluids </option>
<option value="foods"> Foods </option>
<option value="forecasting"> Forecasting </option>
<option value="forensicsci"> Forensic Sciences </option>
<option value="forests"> Forests </option>
<option value="fossstud"> Fossil Studies </option>
<option value="foundations"> Foundations </option>
<option value="fractalfract"> Fractal and Fractional (Fractal Fract) </option>
<option value="fuels"> Fuels </option>
<option value="future"> Future </option>
<option value="futureinternet"> Future Internet </option>
<option value="futurepharmacol"> Future Pharmacology </option>
<option value="futuretransp"> Future Transportation </option>
<option value="galaxies"> Galaxies </option>
<option value="games"> Games </option>
<option value="gases"> Gases </option>
<option value="gastroent"> Gastroenterology Insights </option>
<option value="gastrointestdisord"> Gastrointestinal Disorders </option>
<option value="gastronomy"> Gastronomy </option>
<option value="gels"> Gels </option>
<option value="genealogy"> Genealogy </option>
<option value="genes"> Genes </option>
<option value="geographies"> Geographies </option>
<option value="geohazards"> GeoHazards </option>
<option value="geomatics"> Geomatics </option>
<option value="geometry"> Geometry </option>
<option value="geosciences"> Geosciences </option>
<option value="geotechnics"> Geotechnics </option>
<option value="geriatrics"> Geriatrics </option>
<option value="glacies"> Glacies </option>
<option value="gucdd"> Gout, Urate, and Crystal Deposition Disease (GUCDD) </option>
<option value="grasses"> Grasses </option>
<option value="hardware"> Hardware </option>
<option value="healthcare"> Healthcare </option>
<option value="hearts"> Hearts </option>
<option value="hemato"> Hemato </option>
<option value="hematolrep"> Hematology Reports </option>
<option value="heritage"> Heritage </option>
<option value="histories"> Histories </option>
<option value="horticulturae"> Horticulturae </option>
<option value="hospitals"> Hospitals </option>
<option value="humanities"> Humanities </option>
<option value="humans"> Humans </option>
<option value="hydrobiology"> Hydrobiology </option>
<option value="hydrogen"> Hydrogen </option>
<option value="hydrology"> Hydrology </option>
<option value="hygiene"> Hygiene </option>
<option value="immuno"> Immuno </option>
<option value="idr"> Infectious Disease Reports </option>
<option value="informatics"> Informatics </option>
<option value="information"> Information </option>
<option value="infrastructures"> Infrastructures </option>
<option value="inorganics"> Inorganics </option>
<option value="insects"> Insects </option>
<option value="instruments"> Instruments </option>
<option value="iic"> Intelligent Infrastructure and Construction </option>
<option value="ijerph"> International Journal of Environmental Research and Public Health (IJERPH) </option>
<option value="ijfs"> International Journal of Financial Studies (IJFS) </option>
<option value="ijms"> International Journal of Molecular Sciences (IJMS) </option>
<option value="IJNS"> International Journal of Neonatal Screening (IJNS) </option>
<option value="ijpb"> International Journal of Plant Biology (IJPB) </option>
<option value="ijt"> International Journal of Topology </option>
<option value="ijtm"> International Journal of Translational Medicine (IJTM) </option>
<option value="ijtpp"> International Journal of Turbomachinery, Propulsion and Power (IJTPP) </option>
<option value="ime"> International Medical Education (IME) </option>
<option value="inventions"> Inventions </option>
<option value="IoT"> IoT </option>
<option value="ijgi"> ISPRS International Journal of Geo-Information (IJGI) </option>
<option value="J"> J </option>
<option value="jal"> Journal of Ageing and Longevity (JAL) </option>
<option value="jcdd"> Journal of Cardiovascular Development and Disease (JCDD) </option>
<option value="jcto"> Journal of Clinical & Translational Ophthalmology (JCTO) </option>
<option value="jcm"> Journal of Clinical Medicine (JCM) </option>
<option value="jcs"> Journal of Composites Science (J. Compos. Sci.) </option>
<option value="jcp"> Journal of Cybersecurity and Privacy (JCP) </option>
<option value="jdad"> Journal of Dementia and Alzheimer's Disease (JDAD) </option>
<option value="jdb"> Journal of Developmental Biology (JDB) </option>
<option value="jeta"> Journal of Experimental and Theoretical Analyses (JETA) </option>
<option value="jfb"> Journal of Functional Biomaterials (JFB) </option>
<option value="jfmk"> Journal of Functional Morphology and Kinesiology (JFMK) </option>
<option value="jof"> Journal of Fungi (JoF) </option>
<option value="jimaging"> Journal of Imaging (J. Imaging) </option>
<option value="jintelligence"> Journal of Intelligence (J. Intell.) </option>
<option value="jlpea"> Journal of Low Power Electronics and Applications (JLPEA) </option>
<option value="jmmp"> Journal of Manufacturing and Materials Processing (JMMP) </option>
<option value="jmse"> Journal of Marine Science and Engineering (JMSE) </option>
<option value="jmahp"> Journal of Market Access & Health Policy (JMAHP) </option>
<option value="jmp"> Journal of Molecular Pathology (JMP) </option>
<option value="jnt"> Journal of Nanotheranostics (JNT) </option>
<option value="jne"> Journal of Nuclear Engineering (JNE) </option>
<option value="ohbm"> Journal of Otorhinolaryngology, Hearing and Balance Medicine (JOHBM) </option>
<option value="jop"> Journal of Parks </option>
<option value="jpm"> Journal of Personalized Medicine (JPM) </option>
<option value="jpbi"> Journal of Pharmaceutical and BioTech Industry (JPBI) </option>
<option value="jor"> Journal of Respiration (JoR) </option>
<option value="jrfm"> Journal of Risk and Financial Management (JRFM) </option>
<option value="jsan"> Journal of Sensor and Actuator Networks (JSAN) </option>
<option value="joma"> Journal of the Oman Medical Association (JOMA) </option>
<option value="jtaer"> Journal of Theoretical and Applied Electronic Commerce Research (JTAER) </option>
<option value="jvd"> Journal of Vascular Diseases (JVD) </option>
<option value="jox"> Journal of Xenobiotics (JoX) </option>
<option value="jzbg"> Journal of Zoological and Botanical Gardens (JZBG) </option>
<option value="journalmedia"> Journalism and Media </option>
<option value="kidneydial"> Kidney and Dialysis </option>
<option value="kinasesphosphatases"> Kinases and Phosphatases </option>
<option value="knowledge"> Knowledge </option>
<option value="labmed"> LabMed </option>
<option value="laboratories"> Laboratories </option>
<option value="land"> Land </option>
<option value="languages"> Languages </option>
<option value="laws"> Laws </option>
<option value="life"> Life </option>
<option value="limnolrev"> Limnological Review </option>
<option value="lipidology"> Lipidology </option>
<option value="liquids"> Liquids </option>
<option value="literature"> Literature </option>
<option value="livers"> Livers </option>
<option value="logics"> Logics </option>
<option value="logistics"> Logistics </option>
<option value="lubricants"> Lubricants </option>
<option value="lymphatics"> Lymphatics </option>
<option value="make"> Machine Learning and Knowledge Extraction (MAKE) </option>
<option value="machines"> Machines </option>
<option value="macromol"> Macromol </option>
<option value="magnetism"> Magnetism </option>
<option value="magnetochemistry"> Magnetochemistry </option>
<option value="marinedrugs"> Marine Drugs </option>
<option value="materials"> Materials </option>
<option value="materproc"> Materials Proceedings </option>
<option value="mca"> Mathematical and Computational Applications (MCA) </option>
<option value="mathematics"> Mathematics </option>
<option value="medsci"> Medical Sciences </option>
<option value="msf"> Medical Sciences Forum </option>
<option value="medicina"> Medicina </option>
<option value="medicines"> Medicines </option>
<option value="membranes"> Membranes </option>
<option value="merits"> Merits </option>
<option value="metabolites"> Metabolites </option>
<option value="metals"> Metals </option>
<option value="meteorology"> Meteorology </option>
<option value="methane"> Methane </option>
<option value="mps"> Methods and Protocols (MPs) </option>
<option value="metrics"> Metrics </option>
<option value="metrology"> Metrology </option>
<option value="micro"> Micro </option>
<option value="microbiolres"> Microbiology Research </option>
<option value="micromachines"> Micromachines </option>
<option value="microorganisms"> Microorganisms </option>
<option value="microplastics"> Microplastics </option>
<option value="minerals"> Minerals </option>
<option value="mining"> Mining </option>
<option value="modelling"> Modelling </option>
<option value="mmphys"> Modern Mathematical Physics </option>
<option value="molbank"> Molbank </option>
<option value="molecules"> Molecules </option>
<option value="mti"> Multimodal Technologies and Interaction (MTI) </option>
<option value="muscles"> Muscles </option>
<option value="nanoenergyadv"> Nanoenergy Advances </option>
<option value="nanomanufacturing"> Nanomanufacturing </option>
<option value="nanomaterials"> Nanomaterials </option>
<option value="ndt"> NDT </option>
<option value="network"> Network </option>
<option value="neuroglia"> Neuroglia </option>
<option value="neurolint"> Neurology International </option>
<option value="neurosci"> NeuroSci </option>
<option value="nitrogen"> Nitrogen </option>
<option value="ncrna"> Non-Coding RNA (ncRNA) </option>
<option value="nursrep"> Nursing Reports </option>
<option value="nutraceuticals"> Nutraceuticals </option>
<option value="nutrients"> Nutrients </option>
<option value="obesities"> Obesities </option>
<option value="oceans"> Oceans </option>
<option value="onco"> Onco </option>
<option value="optics"> Optics </option>
<option value="oral"> Oral </option>
<option value="organics"> Organics </option>
<option value="organoids"> Organoids </option>
<option value="osteology"> Osteology </option>
<option value="oxygen"> Oxygen </option>
<option value="parasitologia"> Parasitologia </option>
<option value="particles"> Particles </option>
<option value="pathogens"> Pathogens </option>
<option value="pathophysiology"> Pathophysiology </option>
<option value="pediatrrep"> Pediatric Reports </option>
<option value="pets"> Pets </option>
<option value="pharmaceuticals"> Pharmaceuticals </option>
<option value="pharmaceutics"> Pharmaceutics </option>
<option value="pharmacoepidemiology"> Pharmacoepidemiology </option>
<option value="pharmacy"> Pharmacy </option>
<option value="philosophies"> Philosophies </option>
<option value="photochem"> Photochem </option>
<option value="photonics"> Photonics </option>
<option value="phycology"> Phycology </option>
<option value="physchem"> Physchem </option>
<option value="psf"> Physical Sciences Forum </option>
<option value="physics"> Physics </option>
<option value="physiologia"> Physiologia </option>
<option value="plants"> Plants </option>
<option value="plasma"> Plasma </option>
<option value="platforms"> Platforms </option>
<option value="pollutants"> Pollutants </option>
<option value="polymers"> Polymers </option>
<option value="polysaccharides"> Polysaccharides </option>
<option value="populations"> Populations </option>
<option value="poultry"> Poultry </option>
<option value="powders"> Powders </option>
<option value="proceedings"> Proceedings </option>
<option value="processes"> Processes </option>
<option value="prosthesis"> Prosthesis </option>
<option value="proteomes"> Proteomes </option>
<option value="psychiatryint"> Psychiatry International </option>
<option value="psychoactives"> Psychoactives </option>
<option value="psycholint"> Psychology International </option>
<option value="publications"> Publications </option>
<option value="qubs"> Quantum Beam Science (QuBS) </option>
<option value="quantumrep"> Quantum Reports </option>
<option value="quaternary"> Quaternary </option>
<option value="radiation"> Radiation </option>
<option value="reactions"> Reactions </option>
<option value="realestate"> Real Estate </option>
<option value="receptors"> Receptors </option>
<option value="recycling"> Recycling </option>
<option value="rsee"> Regional Science and Environmental Economics (RSEE) </option>
<option value="religions"> Religions </option>
<option value="remotesensing"> Remote Sensing </option>
<option value="reports"> Reports </option>
<option value="reprodmed"> Reproductive Medicine (Reprod. Med.) </option>
<option value="resources"> Resources </option>
<option value="rheumato"> Rheumato </option>
<option value="risks"> Risks </option>
<option value="robotics"> Robotics </option>
<option value="ruminants"> Ruminants </option>
<option value="safety"> Safety </option>
<option value="sci"> Sci </option>
<option value="scipharm"> Scientia Pharmaceutica (Sci. Pharm.) </option>
<option value="sclerosis"> Sclerosis </option>
<option value="seeds"> Seeds </option>
<option value="sensors"> Sensors </option>
<option value="separations"> Separations </option>
<option value="sexes"> Sexes </option>
<option value="signals"> Signals </option>
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<option value="175920">Advances in the Functional Evaluation and Biomechanics of the Human Movement</option>
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<option value="42938">From Yeast to Biotechnology</option>
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<option value="194176">Innovative Approaches in Assistive Technology and Rehabilitation</option>
<option value="146487">Innovative Medical Technology and Surgical Techniques: Focus on Joint Arthroplasty</option>
<option value="105921">Life Science and Technology Research in Huazhong University of Science and Technology: Commemorating the University’s 70th Anniversary</option>
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<option value="6018">Mesoporous Materials in Drug Delivery and Regenerative Engineering 2016</option>
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<option value="48087">Microbial Biopolymers: From Synthesis to Properties and Applications</option>
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<option value="103550">Molecular Diagnostics in Postgenomic Era</option>
<option value="7200">Nanoparticle Interaction with the Biological Milieu</option>
<option value="43573">New Bioinformatics Tools</option>
<option value="122403">New Devices and Preclinical Assessment for the Treatment of Mitral and Tricuspid Regurgitation</option>
<option value="167286">New Insights into the Development of In Situ Bioprinting for Tissue Engineering</option>
<option value="105221">New Sight of Biomaterials and Tissue Regeneration in Medicine Research: Updates and Future Direction</option>
<option value="206713">New Sights of Deep Learning and Biomedical Image Processing: Updates, Applications, and Directions</option>
<option value="16034">Noble Metal Functionalized Nanoparticles for Biomedical Applications</option>
<option value="212715">Numerical Simulation and AI in Biological Systems</option>
<option value="15932">Ophthalmic Biomaterials</option>
<option value="20854">Organs-on-Chips</option>
<option value="50619">Organs-on-Chips, Volume 2</option>
<option value="112530">Plant Bioengineering and Omics for Improving Crop Productivity, Stress Tolerance, and Industrial Applications</option>
<option value="57515">Protein-based Biomaterials: Reaching to the Future by Learning from the Past</option>
<option value="58769">Quantification of Biological and Mechanical Changes Due to Radiation Exposure</option>
<option value="108693">Recent Advance in Epilepsy and Brain Mapping</option>
<option value="105218">Recent Advances in Biomaterials and Dental Disease</option>
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<option value="109364">Recent Progress in Advanced Therapy Medicinal Products: Scaffold Design, Cell Priming and Genetic Engineering</option>
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<option value="150472">Role of Computational Methods for Living Systems at Different Scales</option>
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<option value="173322">Smart Engineering: Integrating Artificial Intelligence and Bioengineering</option>
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<option value="111683">State of the Art in Interdisciplinary Microfluidics</option>
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<option value="188706">The 2nd Edition: Signal and Image Processing in Biomedical Applications Using Machine Learning</option>
<option value="49040">The Next Generation of Prosthetic Heart Valves</option>
<option value="87680">The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems</option>
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<option value="101504">Women's Special Issue Series: Biosensors</option>
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<select multiple="" id="newsletter-journal" class="foundation-select jqmsLoaded" name="journals[]" style="display: none;">
<option value="acoustics">Acoustics</option>
<option value="amh">Acta Microbiologica Hellenica</option>
<option value="actuators">Actuators</option>
<option value="admsci">Administrative Sciences</option>
<option value="adolescents">Adolescents</option>
<option value="arm">Advances in Respiratory Medicine</option>
<option value="aerobiology">Aerobiology</option>
<option value="aerospace">Aerospace</option>
<option value="agriculture">Agriculture</option>
<option value="agriengineering">AgriEngineering</option>
<option value="agrochemicals">Agrochemicals</option>
<option value="agronomy">Agronomy</option>
<option value="ai">AI</option>
<option value="air">Air</option>
<option value="algorithms">Algorithms</option>
<option value="allergies">Allergies</option>
<option value="alloys">Alloys</option>
<option value="analytica">Analytica</option>
<option value="analytics">Analytics</option>
<option value="anatomia">Anatomia</option>
<option value="anesthres">Anesthesia Research</option>
<option value="animals">Animals</option>
<option value="antibiotics">Antibiotics</option>
<option value="antibodies">Antibodies</option>
<option value="antioxidants">Antioxidants</option>
<option value="applbiosci">Applied Biosciences</option>
<option value="applmech">Applied Mechanics</option>
<option value="applmicrobiol">Applied Microbiology</option>
<option value="applnano">Applied Nano</option>
<option value="applsci">Applied Sciences</option>
<option value="asi">Applied System Innovation</option>
<option value="appliedchem">AppliedChem</option>
<option value="appliedmath">AppliedMath</option>
<option value="aquacj">Aquaculture Journal</option>
<option value="architecture">Architecture</option>
<option value="arthropoda">Arthropoda</option>
<option value="arts">Arts</option>
<option value="astronomy">Astronomy</option>
<option value="atmosphere">Atmosphere</option>
<option value="atoms">Atoms</option>
<option value="audiolres">Audiology Research</option>
<option value="automation">Automation</option>
<option value="axioms">Axioms</option>
<option value="bacteria">Bacteria</option>
<option value="batteries">Batteries</option>
<option value="behavsci">Behavioral Sciences</option>
<option value="beverages">Beverages</option>
<option value="BDCC">Big Data and Cognitive Computing</option>
<option value="biochem">BioChem</option>
<option value="bioengineering">Bioengineering</option>
<option value="biologics">Biologics</option>
<option value="biology">Biology</option>
<option value="blsf">Biology and Life Sciences Forum</option>
<option value="biomass">Biomass</option>
<option value="biomechanics">Biomechanics</option>
<option value="biomed">BioMed</option>
<option value="biomedicines">Biomedicines</option>
<option value="biomedinformatics">BioMedInformatics</option>
<option value="biomimetics">Biomimetics</option>
<option value="biomolecules">Biomolecules</option>
<option value="biophysica">Biophysica</option>
<option value="biosensors">Biosensors</option>
<option value="biotech">BioTech</option>
<option value="birds">Birds</option>
<option value="blockchains">Blockchains</option>
<option value="brainsci">Brain Sciences</option>
<option value="buildings">Buildings</option>
<option value="businesses">Businesses</option>
<option value="carbon">C</option>
<option value="cancers">Cancers</option>
<option value="cardiogenetics">Cardiogenetics</option>
<option value="catalysts">Catalysts</option>
<option value="cells">Cells</option>
<option value="ceramics">Ceramics</option>
<option value="challenges">Challenges</option>
<option value="ChemEngineering">ChemEngineering</option>
<option value="chemistry">Chemistry</option>
<option value="chemproc">Chemistry Proceedings</option>
<option value="chemosensors">Chemosensors</option>
<option value="children">Children</option>
<option value="chips">Chips</option>
<option value="civileng">CivilEng</option>
<option value="cleantechnol">Clean Technologies</option>
<option value="climate">Climate</option>
<option value="ctn">Clinical and Translational Neuroscience</option>
<option value="clinpract">Clinics and Practice</option>
<option value="clockssleep">Clocks & Sleep</option>
<option value="coasts">Coasts</option>
<option value="coatings">Coatings</option>
<option value="colloids">Colloids and Interfaces</option>
<option value="colorants">Colorants</option>
<option value="commodities">Commodities</option>
<option value="complications">Complications</option>
<option value="compounds">Compounds</option>
<option value="computation">Computation</option>
<option value="csmf">Computer Sciences & Mathematics Forum</option>
<option value="computers">Computers</option>
<option value="condensedmatter">Condensed Matter</option>
<option value="conservation">Conservation</option>
<option value="constrmater">Construction Materials</option>
<option value="cmd">Corrosion and Materials Degradation</option>
<option value="cosmetics">Cosmetics</option>
<option value="covid">COVID</option>
<option value="crops">Crops</option>
<option value="cryo">Cryo</option>
<option value="cryptography">Cryptography</option>
<option value="crystals">Crystals</option>
<option value="cimb">Current Issues in Molecular Biology</option>
<option value="curroncol">Current Oncology</option>
<option value="dairy">Dairy</option>
<option value="data">Data</option>
<option value="dentistry">Dentistry Journal</option>
<option value="dermato">Dermato</option>
<option value="dermatopathology">Dermatopathology</option>
<option value="designs">Designs</option>
<option value="diabetology">Diabetology</option>
<option value="diagnostics">Diagnostics</option>
<option value="dietetics">Dietetics</option>
<option value="digital">Digital</option>
<option value="disabilities">Disabilities</option>
<option value="diseases">Diseases</option>
<option value="diversity">Diversity</option>
<option value="dna">DNA</option>
<option value="drones">Drones</option>
<option value="ddc">Drugs and Drug Candidates</option>
<option value="dynamics">Dynamics</option>
<option value="earth">Earth</option>
<option value="ecologies">Ecologies</option>
<option value="econometrics">Econometrics</option>
<option value="economies">Economies</option>
<option value="education">Education Sciences</option>
<option value="electricity">Electricity</option>
<option value="electrochem">Electrochem</option>
<option value="electronicmat">Electronic Materials</option>
<option value="electronics">Electronics</option>
<option value="ecm">Emergency Care and Medicine</option>
<option value="encyclopedia">Encyclopedia</option>
<option value="endocrines">Endocrines</option>
<option value="energies">Energies</option>
<option value="esa">Energy Storage and Applications</option>
<option value="eng">Eng</option>
<option value="engproc">Engineering Proceedings</option>
<option value="entropy">Entropy</option>
<option value="environsciproc">Environmental Sciences Proceedings</option>
<option value="environments">Environments</option>
<option value="epidemiologia">Epidemiologia</option>
<option value="epigenomes">Epigenomes</option>
<option value="ebj">European Burn Journal</option>
<option value="ejihpe">European Journal of Investigation in Health, Psychology and Education</option>
<option value="fermentation">Fermentation</option>
<option value="fibers">Fibers</option>
<option value="fintech">FinTech</option>
<option value="fire">Fire</option>
<option value="fishes">Fishes</option>
<option value="fluids">Fluids</option>
<option value="foods">Foods</option>
<option value="forecasting">Forecasting</option>
<option value="forensicsci">Forensic Sciences</option>
<option value="forests">Forests</option>
<option value="fossstud">Fossil Studies</option>
<option value="foundations">Foundations</option>
<option value="fractalfract">Fractal and Fractional</option>
<option value="fuels">Fuels</option>
<option value="future">Future</option>
<option value="futureinternet">Future Internet</option>
<option value="futurepharmacol">Future Pharmacology</option>
<option value="futuretransp">Future Transportation</option>
<option value="galaxies">Galaxies</option>
<option value="games">Games</option>
<option value="gases">Gases</option>
<option value="gastroent">Gastroenterology Insights</option>
<option value="gastrointestdisord">Gastrointestinal Disorders</option>
<option value="gastronomy">Gastronomy</option>
<option value="gels">Gels</option>
<option value="genealogy">Genealogy</option>
<option value="genes">Genes</option>
<option value="geographies">Geographies</option>
<option value="geohazards">GeoHazards</option>
<option value="geomatics">Geomatics</option>
<option value="geometry">Geometry</option>
<option value="geosciences">Geosciences</option>
<option value="geotechnics">Geotechnics</option>
<option value="geriatrics">Geriatrics</option>
<option value="glacies">Glacies</option>
<option value="gucdd">Gout, Urate, and Crystal Deposition Disease</option>
<option value="grasses">Grasses</option>
<option value="hardware">Hardware</option>
<option value="healthcare">Healthcare</option>
<option value="hearts">Hearts</option>
<option value="hemato">Hemato</option>
<option value="hematolrep">Hematology Reports</option>
<option value="heritage">Heritage</option>
<option value="histories">Histories</option>
<option value="horticulturae">Horticulturae</option>
<option value="hospitals">Hospitals</option>
<option value="humanities">Humanities</option>
<option value="humans">Humans</option>
<option value="hydrobiology">Hydrobiology</option>
<option value="hydrogen">Hydrogen</option>
<option value="hydrology">Hydrology</option>
<option value="hygiene">Hygiene</option>
<option value="immuno">Immuno</option>
<option value="idr">Infectious Disease Reports</option>
<option value="informatics">Informatics</option>
<option value="information">Information</option>
<option value="infrastructures">Infrastructures</option>
<option value="inorganics">Inorganics</option>
<option value="insects">Insects</option>
<option value="instruments">Instruments</option>
<option value="iic">Intelligent Infrastructure and Construction</option>
<option value="ijerph">International Journal of Environmental Research and Public Health</option>
<option value="ijfs">International Journal of Financial Studies</option>
<option value="ijms">International Journal of Molecular Sciences</option>
<option value="IJNS">International Journal of Neonatal Screening</option>
<option value="ijpb">International Journal of Plant Biology</option>
<option value="ijt">International Journal of Topology</option>
<option value="ijtm">International Journal of Translational Medicine</option>
<option value="ijtpp">International Journal of Turbomachinery, Propulsion and Power</option>
<option value="ime">International Medical Education</option>
<option value="inventions">Inventions</option>
<option value="IoT">IoT</option>
<option value="ijgi">ISPRS International Journal of Geo-Information</option>
<option value="J">J</option>
<option value="jal">Journal of Ageing and Longevity</option>
<option value="jcdd">Journal of Cardiovascular Development and Disease</option>
<option value="jcto">Journal of Clinical & Translational Ophthalmology</option>
<option value="jcm">Journal of Clinical Medicine</option>
<option value="jcs">Journal of Composites Science</option>
<option value="jcp">Journal of Cybersecurity and Privacy</option>
<option value="jdad">Journal of Dementia and Alzheimer's Disease</option>
<option value="jdb">Journal of Developmental Biology</option>
<option value="jeta">Journal of Experimental and Theoretical Analyses</option>
<option value="jfb">Journal of Functional Biomaterials</option>
<option value="jfmk">Journal of Functional Morphology and Kinesiology</option>
<option value="jof">Journal of Fungi</option>
<option value="jimaging">Journal of Imaging</option>
<option value="jintelligence">Journal of Intelligence</option>
<option value="jlpea">Journal of Low Power Electronics and Applications</option>
<option value="jmmp">Journal of Manufacturing and Materials Processing</option>
<option value="jmse">Journal of Marine Science and Engineering</option>
<option value="jmahp">Journal of Market Access & Health Policy</option>
<option value="jmp">Journal of Molecular Pathology</option>
<option value="jnt">Journal of Nanotheranostics</option>
<option value="jne">Journal of Nuclear Engineering</option>
<option value="ohbm">Journal of Otorhinolaryngology, Hearing and Balance Medicine</option>
<option value="jop">Journal of Parks</option>
<option value="jpm">Journal of Personalized Medicine</option>
<option value="jpbi">Journal of Pharmaceutical and BioTech Industry</option>
<option value="jor">Journal of Respiration</option>
<option value="jrfm">Journal of Risk and Financial Management</option>
<option value="jsan">Journal of Sensor and Actuator Networks</option>
<option value="joma">Journal of the Oman Medical Association</option>
<option value="jtaer">Journal of Theoretical and Applied Electronic Commerce Research</option>
<option value="jvd">Journal of Vascular Diseases</option>
<option value="jox">Journal of Xenobiotics</option>
<option value="jzbg">Journal of Zoological and Botanical Gardens</option>
<option value="journalmedia">Journalism and Media</option>
<option value="kidneydial">Kidney and Dialysis</option>
<option value="kinasesphosphatases">Kinases and Phosphatases</option>
<option value="knowledge">Knowledge</option>
<option value="labmed">LabMed</option>
<option value="laboratories">Laboratories</option>
<option value="land">Land</option>
<option value="languages">Languages</option>
<option value="laws">Laws</option>
<option value="life">Life</option>
<option value="limnolrev">Limnological Review</option>
<option value="lipidology">Lipidology</option>
<option value="liquids">Liquids</option>
<option value="literature">Literature</option>
<option value="livers">Livers</option>
<option value="logics">Logics</option>
<option value="logistics">Logistics</option>
<option value="lubricants">Lubricants</option>
<option value="lymphatics">Lymphatics</option>
<option value="make">Machine Learning and Knowledge Extraction</option>
<option value="machines">Machines</option>
<option value="macromol">Macromol</option>
<option value="magnetism">Magnetism</option>
<option value="magnetochemistry">Magnetochemistry</option>
<option value="marinedrugs">Marine Drugs</option>
<option value="materials">Materials</option>
<option value="materproc">Materials Proceedings</option>
<option value="mca">Mathematical and Computational Applications</option>
<option value="mathematics">Mathematics</option>
<option value="medsci">Medical Sciences</option>
<option value="msf">Medical Sciences Forum</option>
<option value="medicina">Medicina</option>
<option value="medicines">Medicines</option>
<option value="membranes">Membranes</option>
<option value="merits">Merits</option>
<option value="metabolites">Metabolites</option>
<option value="metals">Metals</option>
<option value="meteorology">Meteorology</option>
<option value="methane">Methane</option>
<option value="mps">Methods and Protocols</option>
<option value="metrics">Metrics</option>
<option value="metrology">Metrology</option>
<option value="micro">Micro</option>
<option value="microbiolres">Microbiology Research</option>
<option value="micromachines">Micromachines</option>
<option value="microorganisms">Microorganisms</option>
<option value="microplastics">Microplastics</option>
<option value="minerals">Minerals</option>
<option value="mining">Mining</option>
<option value="modelling">Modelling</option>
<option value="mmphys">Modern Mathematical Physics</option>
<option value="molbank">Molbank</option>
<option value="molecules">Molecules</option>
<option value="mti">Multimodal Technologies and Interaction</option>
<option value="muscles">Muscles</option>
<option value="nanoenergyadv">Nanoenergy Advances</option>
<option value="nanomanufacturing">Nanomanufacturing</option>
<option value="nanomaterials">Nanomaterials</option>
<option value="ndt">NDT</option>
<option value="network">Network</option>
<option value="neuroglia">Neuroglia</option>
<option value="neurolint">Neurology International</option>
<option value="neurosci">NeuroSci</option>
<option value="nitrogen">Nitrogen</option>
<option value="ncrna">Non-Coding RNA</option>
<option value="nursrep">Nursing Reports</option>
<option value="nutraceuticals">Nutraceuticals</option>
<option value="nutrients">Nutrients</option>
<option value="obesities">Obesities</option>
<option value="oceans">Oceans</option>
<option value="onco">Onco</option>
<option value="optics">Optics</option>
<option value="oral">Oral</option>
<option value="organics">Organics</option>
<option value="organoids">Organoids</option>
<option value="osteology">Osteology</option>
<option value="oxygen">Oxygen</option>
<option value="parasitologia">Parasitologia</option>
<option value="particles">Particles</option>
<option value="pathogens">Pathogens</option>
<option value="pathophysiology">Pathophysiology</option>
<option value="pediatrrep">Pediatric Reports</option>
<option value="pets">Pets</option>
<option value="pharmaceuticals">Pharmaceuticals</option>
<option value="pharmaceutics">Pharmaceutics</option>
<option value="pharmacoepidemiology">Pharmacoepidemiology</option>
<option value="pharmacy">Pharmacy</option>
<option value="philosophies">Philosophies</option>
<option value="photochem">Photochem</option>
<option value="photonics">Photonics</option>
<option value="phycology">Phycology</option>
<option value="physchem">Physchem</option>
<option value="psf">Physical Sciences Forum</option>
<option value="physics">Physics</option>
<option value="physiologia">Physiologia</option>
<option value="plants">Plants</option>
<option value="plasma">Plasma</option>
<option value="platforms">Platforms</option>
<option value="pollutants">Pollutants</option>
<option value="polymers">Polymers</option>
<option value="polysaccharides">Polysaccharides</option>
<option value="populations">Populations</option>
<option value="poultry">Poultry</option>
<option value="powders">Powders</option>
<option value="proceedings">Proceedings</option>
<option value="processes">Processes</option>
<option value="prosthesis">Prosthesis</option>
<option value="proteomes">Proteomes</option>
<option value="psychiatryint">Psychiatry International</option>
<option value="psychoactives">Psychoactives</option>
<option value="psycholint">Psychology International</option>
<option value="publications">Publications</option>
<option value="qubs">Quantum Beam Science</option>
<option value="quantumrep">Quantum Reports</option>
<option value="quaternary">Quaternary</option>
<option value="radiation">Radiation</option>
<option value="reactions">Reactions</option>
<option value="realestate">Real Estate</option>
<option value="receptors">Receptors</option>
<option value="recycling">Recycling</option>
<option value="rsee">Regional Science and Environmental Economics</option>
<option value="religions">Religions</option>
<option value="remotesensing">Remote Sensing</option>
<option value="reports">Reports</option>
<option value="reprodmed">Reproductive Medicine</option>
<option value="resources">Resources</option>
<option value="rheumato">Rheumato</option>
<option value="risks">Risks</option>
<option value="robotics">Robotics</option>
<option value="ruminants">Ruminants</option>
<option value="safety">Safety</option>
<option value="sci">Sci</option>
<option value="scipharm">Scientia Pharmaceutica</option>
<option value="sclerosis">Sclerosis</option>
<option value="seeds">Seeds</option>
<option value="sensors">Sensors</option>
<option value="separations">Separations</option>
<option value="sexes">Sexes</option>
<option value="signals">Signals</option>
<option value="sinusitis">Sinusitis</option>
<option value="smartcities">Smart Cities</option>
<option value="socsci">Social Sciences</option>
<option value="siuj">Société Internationale d’Urologie Journal</option>
<option value="societies">Societies</option>
<option value="software">Software</option>
<option value="soilsystems">Soil Systems</option>
<option value="solar">Solar</option>
<option value="solids">Solids</option>
<option value="spectroscj">Spectroscopy Journal</option>
<option value="sports">Sports</option>
<option value="standards">Standards</option>
<option value="stats">Stats</option>
<option value="stresses">Stresses</option>
<option value="surfaces">Surfaces</option>
<option value="surgeries">Surgeries</option>
<option value="std">Surgical Techniques Development</option>
<option value="sustainability">Sustainability</option>
<option value="suschem">Sustainable Chemistry</option>
<option value="symmetry">Symmetry</option>
<option value="synbio">SynBio</option>
<option value="systems">Systems</option>
<option value="targets">Targets</option>
<option value="taxonomy">Taxonomy</option>
<option value="technologies">Technologies</option>
<option value="telecom">Telecom</option>
<option value="textiles">Textiles</option>
<option value="thalassrep">Thalassemia Reports</option>
<option value="therapeutics">Therapeutics</option>
<option value="thermo">Thermo</option>
<option value="timespace">Time and Space</option>
<option value="tomography">Tomography</option>
<option value="tourismhosp">Tourism and Hospitality</option>
<option value="toxics">Toxics</option>
<option value="toxins">Toxins</option>
<option value="transplantology">Transplantology</option>
<option value="traumacare">Trauma Care</option>
<option value="higheredu">Trends in Higher Education</option>
<option value="tropicalmed">Tropical Medicine and Infectious Disease</option>
<option value="universe">Universe</option>
<option value="urbansci">Urban Science</option>
<option value="uro">Uro</option>
<option value="vaccines">Vaccines</option>
<option value="vehicles">Vehicles</option>
<option value="venereology">Venereology</option>
<option value="vetsci">Veterinary Sciences</option>
<option value="vibration">Vibration</option>
<option value="virtualworlds">Virtual Worlds</option>
<option value="viruses">Viruses</option>
<option value="vision">Vision</option>
<option value="waste">Waste</option>
<option value="water">Water</option>
<option value="wild">Wild</option>
<option value="wind">Wind</option>
<option value="women">Women</option>
<option value="world">World</option>
<option value="wevj">World Electric Vehicle Journal</option>
<option value="youth">Youth</option>
<option value="zoonoticdis">Zoonotic Diseases</option>
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<li class=""><label for="ms-opt-358" style="padding-left: 23px;"><input type="checkbox" value="populations" title="Populations" id="ms-opt-358">Populations</label></li>
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<li class=""><label for="ms-opt-364" style="padding-left: 23px;"><input type="checkbox" value="proteomes" title="Proteomes" id="ms-opt-364">Proteomes</label></li>
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<li class=""><label for="ms-opt-405" style="padding-left: 23px;"><input type="checkbox" value="spectroscj" title="Spectroscopy Journal" id="ms-opt-405">Spectroscopy Journal</label></li>
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Open AccessArticle
MULTI-DIMENSIONAL VALIDATION OF THE INTEGRATION OF SYNTACTIC AND SEMANTIC
DISTANCE MEASURES FOR CLUSTERING FIBROMYALGIA PATIENTS IN THE RHEUMATIC MONITOR
BIG DATA STUDY
by
Ayelet Goldstein
Ayelet Goldstein
SciProfiles Scilit Preprints.org Google Scholar
1,†,
Yuval Shahar
Yuval Shahar
SciProfiles Scilit Preprints.org Google Scholar
2,†,
Michal Weisman Raymond
Michal Weisman Raymond
SciProfiles Scilit Preprints.org Google Scholar
2,
Hagit Peleg
Hagit Peleg
SciProfiles Scilit Preprints.org Google Scholar
3,
Eldad Ben-Chetrit
Eldad Ben-Chetrit
SciProfiles Scilit Preprints.org Google Scholar
3,
Arie Ben-Yehuda
Arie Ben-Yehuda
SciProfiles Scilit Preprints.org Google Scholar
4,
Erez Shalom
Erez Shalom
SciProfiles Scilit Preprints.org Google Scholar
2,
Chen Goldstein
Chen Goldstein
SciProfiles Scilit Preprints.org Google Scholar
5,
Shmuel Shay Shiloh
Shmuel Shay Shiloh
SciProfiles Scilit Preprints.org Google Scholar
5 and
Galit Almoznino
Galit Almoznino
SciProfiles Scilit Preprints.org Google Scholar
5,6,*
1
Computer Science Department, Hadassah Academic College, Jerusalem 9101001,
Israel
2
Medical Informatics Research Center, Department of Software and Information
Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501,
Israel
3
Rheumatology Unit, Hadassah Medical Center, Jerusalem 9112102, Israel
4
Division of Internal Medicine, Hadassah Medical Center, Jerusalem 9112102,
Israel
5
Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big
Biomedical Data Research Laboratory, Dean’s Office, Hadassah Medical Center,
Jerusalem 91120, Israel
6
Department of Oral Medicine, Sedation & Maxillofacial Imaging, Hadassah Medical
Center, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem
91120, Israel
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Bioengineering 2024, 11(1), 97; https://doi.org/10.3390/bioengineering11010097
Submission received: 29 November 2023 / Revised: 28 December 2023 / Accepted: 11
January 2024 / Published: 19 January 2024
(This article belongs to the Special Issue New Sight of Intelligent Algorithm
Model and Medical Device in Bioengineering: Updates and Direction)
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ABSTRACT
This study primarily aimed at developing a novel multi-dimensional methodology
to discover and validate the optimal number of clusters. The secondary objective
was to deploy it for the task of clustering fibromyalgia patients. We present a
comprehensive methodology that includes the use of several different clustering
algorithms, quality assessment using several syntactic distance measures (the
Silhouette Index (SI), Calinski–Harabasz index (CHI), and Davies–Bouldin index
(DBI)), stability assessment using the adjusted Rand index (ARI), and the
validation of the internal semantic consistency of each clustering option via
the performance of multiple clustering iterations after the repeated bagging of
the data to select multiple partial data sets. Then, we perform a statistical
analysis of the (clinical) semantics of the most stable clustering options using
the full data set. Finally, the results are validated through a supervised
machine learning (ML) model that classifies the patients back into the
discovered clusters and is interpreted by calculating the Shapley additive
explanations (SHAP) values of the model. Thus, we refer to our methodology as
the clustering, distance measures and iterative statistical and semantic
validation (CDI-SSV) methodology. We applied our method to the analysis of a
comprehensive data set acquired from 1370 fibromyalgia patients. The results
demonstrate that the K-means was highly robust in the syntactic and the internal
consistent semantics analysis phases and was therefore followed by a semantic
assessment to determine the optimal number of clusters (k), which suggested k =
3 as a more clinically meaningful solution, representing three distinct severity
levels. the random forest model validated the results by classification into the
discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP
analysis emphasized the clinical relevance of "functional problems" in
distinguishing the most severe condition. In conclusion, the CDI-SSV methodology
offers significant potential for improving the classification of complex
patients. Our findings suggest a classification system for different profiles of
fibromyalgia patients, which has the potential to improve clinical care, by
providing clinical markers for the evidence-based personalized diagnosis,
management, and prognosis of fibromyalgia patients.
Keywords:
cluster analysis; machine learning algorithm; K-means; Big Data; fibromyalgia;
rheumatic diseases
1. INTRODUCTION
Fibromyalgia represents the most prevalent source associated with chronic
wide-spread musculoskeletal pain, accompanied by fatigue and sleep disturbances,
which are present for at least three months and not explained by any other
medical condition [1]. Fibromyalgia patients may exhibit a variety of other
somatic symptoms including functional impairment and psychiatric symptoms [2].
It is most common in women, and the prevalence rises with age [3,4,5]. The
estimated prevalence is 6.4% (7.7% in women and 4.9% in men) in the United
States [4], and 3.3 to 8.3% in Europe and South America [5]. The etiology and
pathophysiology of fibromyalgia is currently not known, and there is no evidence
of inflammation in the soft tissues [2]. It is considered a pain regulation
disorder, often classified as central sensitization [6], due to alterations in
central nervous system pain and sensory processing [7].
Identifying patient subgroups can assist in comprehending the modifiable risk
factors associated with each cluster and optimize personalized therapeutic
strategies [8]. This is important in fibromyalgia patients, as physicians may
hesitate to accept them due to difficulty in controlling symptoms and a lack of
information about treatments and causes [9]. Prior research identified subgroups
of women with fibromyalgia based on various characteristics, such as pain,
tender points, disability, sensory, cognitive, psychological, or physical
features [10,11,12,13,14]. Previous clustering research on fibromyalgia excluded
patients with trauma history, and comorbid systemic and rheumatological diseases
[10]. However, it is important to include comorbidities and trauma since
fibromyalgia is more frequent in rheumatic diseases [15]. Moreover, up to
one-fourth of the patients had precipitating physical trauma [16], and
psychological trauma; especially, childhood trauma is a risk factor for the
fibromyalgia onset [17].
In recent years, machine learning (ML) has emerged as a pivotal tool in various
fields, including the medical field [18,19] due to its ability to uncover
patterns and insights from complex datasets. For instance, graph-based deep
learning has been utilized for medical diagnosis [20], and inverse reinforcement
learning (IRL) algorithms have optimized performance in complex systems [21].
These advancement in ML, particularly in clustering techniques, have shown
promise in various medical applications [22,23,24]. The potential of ML in
enhancing the understanding and treatment of complex medical conditions like
fibromyalgia is significant, especially given the challenges in subgroup
identification and the need for personalized treatment strategies.
Recent advances in clustering methods lack consensus on optimal methods and
validation approaches. Therefore, the primary aim of our study is to address
this unmet need by developing and evaluating a novel comprehensive
multi-dimensional. clustering methodology. This methodology is designed to be
broadly applicable in various contexts, with a specific emphasis on determining
the optimal number of clusters in a given dataset. The secondary objective is
the application of this developed methodology to the specific case of clustering
fibromyalgia patients. This application is intended to demonstrate the utility
of the methodology in a practical healthcare context, providing insights into
the heterogeneity of fibromyalgia. By implementing the suggested novel
clustering methodology, we aim to identify the optimal clustering approach for
fibromyalgia patients and provide a generalizable method for other clinical
datasets. This study presents a significant contribution to clustering methods
and to clinical knowledge discovery, offering a robust and comprehensive novel
clustering framework. Furthermore, unlike prior research in the fibromyalgia
domain, which included dozens of [14] or several hundred patients [10,11,13],
our study includes 1370 patients with a comprehensive documentation of their
socio-demographics, comorbidities, symptoms, trauma, sleep, pain, functional
problems, and treatment modalities. This enabled us to address the full
heterogeneity of the population of fibromyalgia patients.
2. METHODS
2.1. DATA SOURCE, STUDY PARTICIPANTS AND QUESTIONNAIRE
This research is part of the Rheumatic Monitor study, which focuses on advancing
personalized medicine by identifying patterns that predict the severity of
rheumatic diseases and treatment response [25]. In the Rheumatic Monitor study,
we developed a mobile application for iPhone and Android operating systems that
collects baseline and dynamic questionnaires and includes an option to report on
pain attacks and visualize pain reports. More about the Rheumatic Monitor study
and the application can be found on the research website:
https://www.rheumaticmonitor.org/, accessed on 1 January 2024 [25].
We recruited 1370 fibromyalgia patients voluntarily from an Israeli fibromyalgia
association who responded to a comprehensive questionnaire. In total, 163
features, 151 categorial and 12 numerical, were obtained via a the 28-question
online survey, based on the Rheumatic Monitor application questionnaire,
including variables for painful areas, co-morbidities, sleep problems, and other
domains. The parameters used in the analyses are depicted in Figures 3–7.
Eligibility criteria
Inclusion criteria: Patients aged 18–99 years, with a fibromyalgia diagnosis
given by their rheumatologist.
Exclusion criteria: Patients under 18 years, and pregnant women/breastfeeding
women; patients under 18 years of age due to the need for additional ethical
approvals required for minors and their distinct epidemiological and medical
characteristics.
2.2. ETHICAL APPROVAL
The study received approval from the Institutional Review Board (IRB) of
Hadassah Medical Organization (HMO), approval number 0205-19-HMO. As the study
only entailed anonymous survey analysis, an exemption from informed consent was
granted by the IRB.
2.3. THE CLUSTERING, DISTANCE MEASURES AND ITERATIVE STATISTICAL AND SEMANTIC
VALIDATION (CDI-SSV) METHODOLOGY
We propose a comprehensive multi-dimensional validation methodology for
clustering fibromyalgia patients, integrating both syntactic (based on data’s
quantitative attributes) and semantic (based on meaning) distance measures.
Figure 1 illustrates this methodology. We refer to the first phase of our
methodology as the CDI phase, a syntactic analysis that employs several
clustering algorithms, and distance measures. These are followed by multiple
iterations to evaluate the influence of varying initial seeds, clustering
consistency with partial data, and within and between algorithm clustering
consistency. Subsequently, the SSV phase utilizes statistical analysis to
validate the clinical semantics of the potential clustering options that
survived our rigorous pipeline. Finally, validation of the clusters is conducted
using a supervised machine learning (ML) model to classify the patients back
into the discovered clusters, and the interpretation is further enhanced through
Shapley additive explanations (SHAP) analysis.
Figure 1. The CDI-SSV Methodology: An Integrated Approach for Clustering
Validation. The figure provides an overview of the Clustering, Distance
measures, and Iterative Statistical and Semantic Validation (CDI-SSV)
methodology. The CDI phase serves as the initial step, involving the evaluation
of cluster quality, the impact of different starting seeds, and the consistency
of clusters across various algorithms and pre-defined values of k. Within- and
between-consistency checks, along with evaluations of internal semantic
consistency, are performed to assess the optimal algorithm and values of k. In
the subsequent SSV phase, an external semantic analysis of the results is
conducted, with a particular focus on the clinical context, thus enhancing the
validation process. Finally, machine learning techniques are employed to
validate the results, and their interpretation is facilitated by SHAP (Shapley
additive explanations) values.
2.4. DATA SCALING
Prior to clustering the dataset, we applied feature-wise scaling to the data
using StandardScaler from sklearn.preprocessing, so each feature contributed
equally to the analysis. This standardized each feature to a mean of zero and a
standard deviation of one. Such standardization ensures that features with
larger ranges do not disproportionately influence the clustering, thereby
maintaining comparability across our dataset’s diverse features, such as
clinical and demographic variables. We then applied various clustering
algorithms available in the scikit-learn (sklearn) library in Python [26].
2.5. THE CDI (CLUSTERING, DISTANCE MEASURES, AND ITERATIVE) PHASE
2.5.1. CLUSTERING ALGORITHMS
We evaluated and compared three widely-used clustering algorithms: K-means
[27,28], Gaussian mixture [29,30], and agglomerative clustering [31], utilizing
different linkage methods (complete, ward, average, and single) [32]). These
algorithms were selected for their proven effectiveness in handling diverse data
types and their widespread use in similar studies. For each of these algorithms,
we employed the default parameters as implemented in the scikit-learn (sklearn)
library in Python. This decision was made to ensure consistency with standard
practices in the field and to facilitate reproducibility by other researchers.
We also used Gower’s distance metric [33] as the distance function between data
points, suitable for mixed data types like ours.
2.5.2. DISTANCE MEASURES
SYNTACTIC CLUSTERING-QUALITY EVALUATION METRICS
To assess clustering quality, we used internal metrics like the silhouette index
(SI) [34,35], Davies–Bouldin index (DBI) [36], and Calinski–Harabasz index (CHI)
[37]. These metrics were chosen for their ability to provide a comprehensive
assessment of clustering quality. The SI score provides insights into the
matching of data points to their assigned clusters and neighboring clusters,
with higher scores indicating better matching. The DBI score measures the
separation between clusters, with lower scores indicating better separation. The
CHI score indicates the degree of cluster definition, with higher scores
representing better-defined clusters. For each algorithm and number k of
clusters, we calculated the SI, DBI, and CHI. In addition to these metrics, we
employed the adjusted Rand index (ARI) to quantify the similarity between two
clustering solutions. The ARI score ranges from −1 to 1 (0: random correlation;
1: perfect correlation). These metrics collectively offer a balanced evaluation
of cluster cohesion and separation, essential for our study’s objectives.
ASSESSMENT OF THE CLUSTERING’S QUALITY VIA MULTIPLE SYNTACTIC DISTANCE
EVALUATION METRICS
To assess the robustness and stability of the clustering algorithms under
various conditions, we employed several approaches [38,39,40]. We computed three
evaluation distance measures metrics (the SI, CHI, and DBI) for each algorithm
(K-means, Gaussian mixture, and agglomerative clustering using all four linkage
methods), and for each value of k, with and without the use of Gower’s distance
metric. This allowed us to compare the performance of the different algorithms
and examine the impact of the number of clusters (k) on the quality metrics.
2.5.3. ITERATIVE PHASE
We tested the stability of our algorithms under different conditions, such as
varying starting seeds, and using subsets of data. This helped us ensure the
reliability of our clustering results.
ASSESSING THE CLUSTERING’S SENSITIVITY TO STARTING SEEDS
We conducted a thorough evaluation to examine the impact of initial seeds on the
performance of the K-means and Gaussian algorithms. To assess their sensitivity,
we performed 30 iterations of each algorithm, both with and without the
utilization of the Gower’s metric. This evaluation used various k values,
employing SI, CHI, and DBI as the evaluation metrics. The results were presented
in a box plot showcasing the mean score index across all runs. This analysis
allowed us to assess the stability of algorithms under diverse starting
conditions.
WITHIN AND BETWEEN CLUSTERING CONSISTENCY USING THE ADJUSTED RAND INDEX (ARI)
We performed 30 iterations using a randomly selected subset amounting to 70% of
the data to assess cluster consistency. We counted the number of “bad clusters”
defined as clusters containing <5% of the data, and calculated the SI, CHI, and
DBI scores for each algorithm and k value. The mean, standard deviation, and
distribution of these scores were analyzed using box plots.
WITHIN AND BETWEEN CLUSTERING CONSISTENCY USING THE ADJUSTED RAND INDEX (ARI)
To evaluate the overall clustering consistency, we applied each algorithm to the
dataset for 10 iterations using random seeds. We saved the resulting labels
after each iteration. Intra-algorithm consistency was assessed by calculating
ARI scores for all possible pairs of labels (45 pairs in total for 10
iterations), assessing the consistency of patient assignment to the same cluster
across different iterations, seeds, or metrics for each algorithm. Additionally,
inter-algorithm similarity was examined by comparing the results of two
different algorithms, aiming to verify the consistency of patient assignment
with different algorithms.
INTERNAL SEMANTIC ASSESSMENT THROUGH MULTIPLE BAGGING ITERATIONS USING PARTIAL
SUBSETS (70%) OF THE DATA
In addition to the internal evaluation metrics and ARI scores, we conducted a
semantic evaluation of the clustering results. For each k value, we clustered
the dataset 10 times using a random 70% subset selected through bagging. To
assign semantic labels to clusters across iterations, we manually identified
semantically similar clusters based on key clinical features, to ensure that
those with similar semantics had the same label. For example, we consistently
labeled cluster “Ci,0” from iterations i = 1 to 10 as “Cluster 0”, which
represented the cluster that appeared to be the “sickest” in each iteration. We
identified cluster semantics using aggregative features, such as the sum of pain
locations, and compared the proportions of categorical demographic and clinical
features (e.g., percentage of females) among clusters with different semantics
generated in different iterations. Specifically, we compared Cluster
𝐶_(𝑖,𝑘𝑚)C_(i,km) (e.g., the semantically identified sickest cluster,
generated for k = k, in iteration i) to Cluster 𝐶_(𝑗,𝑘𝑚)C_(j,km) for 1 ≤ i,
j ≤ 10, i ≠ j (e.g., the semantically identified sickest cluster generated in
each of the 10 iterations). This comparison was performed for all m = 1..k
clusters, resulting in 45 × k pairs of clustering instances being compared. We
employed a Z proportion test to calculate the difference in proportion of each
of the 151 categorical features for each cluster. This analysis helped us to
assess the consistency of cluster semantics across iterations and identify
potential sources of variability in the clustering results.
2.6. THE SSV (STATISTICAL AND SEMANTIC VALIDATION) PHASE
Once the method and the optimal number of k clusters were determined, we moved
to the SSV phase. Here, we statistically validated the clusters’ clinical
relevance by analyzing associations with various patient features.
2.6.1. EXTERNAL (CLINICAL) SEMANTIC ASSESSMENT USING STATISTICAL ANALYSIS
To statistically evaluate the selected clusters, we analyzed the associations of
the clusters with continuous and categorical features. For continuous features,
we calculated the mean and standard deviation and employed the student t-test (k
= 2) or ANOVA corrected with Bonferroni (k > 2) to examine the differences in
cluster distributions. For categorical features, we computed frequencies and
percentages and utilized either Pearson’s chi-square test (k = 2) or the
likelihood ratio test (k > 2). The significance level was set at 0.05 to
determine the statistical significance of the observed results.
2.6.2. CLUSTER VALIDATION AND INTERPRETATION USING MACHINE LEARNING AND SHAP
To validate the clusters identified, we used a random forest model to predict
the cluster assignments for each patient. For this model, we utilized the
default parameters as implemented in the scikit-learn (sklearn) library in
Python. This machine learning approach was chosen for its robustness and ability
to handle complex, multi-dimensional data. Further, to understand which features
most influenced these predictions, we utilized SHAP values. SHAP values provide
insights into the contribution of each feature to the prediction made by the
model, thereby clarifying which features are most influential in defining each
cluster, enhancing the interpretation of the clustering results. To facilitate
this computation, we utilized the TreeExplainer method, designed for tree-based
models [37,38]; like random forest, this method allows for an efficient and
accurate interpretation of the model’s output. Moreover, to enhance
interpretability, we grouped features into aggregative sums, enabling us to
analyze the collective impact of related features on the clustering, providing a
more holistic view of the factors that differentiated the patient clusters.
3. RESULTS
3.1. RESULTS OF THE CDI PHASE
3.1.1. RESULTS OF THE CLUSTERING PHASE
In total, 1370 subjects were included in the analysis. Initially, we employed
principal component analysis (PCA) [41,42] to visualize the outcomes of various
clustering algorithms across different k values. The PCA analysis incorporated
88 components, which accounted for over 80% of the data’s variance. For a visual
representation of each algorithm across different k values, refer to Figure A1,
Appendix A. The visualizations indicate that the K-means and Gaussian clustering
methods exhibit greater similarity in their cluster assignments compared to
those under the agglomerative clustering method using the Ward linkage
criterion. Interestingly, applying different linkage criteria to the
agglomerative method often resulted in most data points being assigned to a
single cluster, suggesting that linkage criteria other than that of Ward may
yield less meaningful cluster assignments.
Additionally, we examined the impact of various linkage criteria on
agglomerative clustering results as illustrated by a dendrogram in Figure A2,
Appendix A. The dendrogram reinforces our observation that employing linkage
criteria other than that of Ward tends to result in less meaningful cluster
assignments. Consequently, the careful selection of an appropriate linkage
criterion is crucial for achieving meaningful results in agglomerative
clustering.
3.1.2. RESULTS OF THE DISTANCE MEASURE PHASE
The evaluation metrics (SI, CHI, and DBI) were employed to assess the quality of
clusters generated by the K-means, Gaussian mixture, and agglomerative Ward
algorithms for various k values. These results are depicted in Figure 2.
Figure 2. Evaluation of clustering algorithms using evaluation metrics. (A)
Silhouette index (SI): the SI scores for different values of k indicate that
K-means with Gower’s distance metric achieved the highest score for k = 2, 3,
and 5. (B) Calinski–Harabasz index (CHI): K-means consistently outperformed
other algorithms, achieving the best score across all values of k. (C)
Davies–Bouldin index (DBI): K-means demonstrated superior results for all values
of k.
For clarity, we omitted results from agglomerative algorithms with linkages that
clustered almost all points into a single cluster. However, their results can be
found in Figure A3, Figure A4 and Figure A5, Appendix A.
SILHOUETTE INDEX (SI)
The SI measure displayed in Figure 2A shows that using Gower’s distance metric
improved the results. Specifically, K-means with Gower’s distance metric
achieved the highest SI score for k = 2, 3, and 5, followed by Gaussian mixture
with Gower’s distance metric, which exhibited a slightly better score for k = 4.
The agglomerative Ward algorithm performed relatively worse across most k
values. Additionally, K-means outperformed Gaussian mixture for k = 1 and 5 but
not for k = 2 and 3. Notably, the SI score tends to decline with an increasing k
value in almost all algorithms, except for K-means, where it remains relatively
consistent for k = 3, 4, and 5.
CALINSKI–HARABASZ INDEX (CHI)
Figure 2B illustrates the CHI measure. K-means consistently achieved the highest
score for all k values, followed by Gaussian mixture and the agglomerative
algorithm with Ward linkage. Interestingly, the use of Gower’s metric led to
inferior results. The CHI score also decreased as k increased.
DAVIES-BOULDIN INDEX (DBI)
The DBI measure is depicted in Figure 2C. The use of Gower’s metric
significantly worsened the results, leading to their exclusion from Figure 2C.
K-means consistently attained the best (lowest) DBI score across all k values.
Gaussian mixture outperformed the agglomerative algorithm with Ward linkage for
k = 2 and k = 3 but not for k = 4 and k = 5. Unlike the SI and CHI scores, no
improvement in the DBI score was observed as the k increased.
In summary, Figure 2 shows that K-means outperformed the other algorithms in two
of the three evaluation metrics. Specifically, in terms of the CHI score,
K-means demonstrated superior performance across all k values, surpassing all
other algorithms. Additionally, for the DBI score, K-means achieved the best
(lowest and thus best) score across all k values after excluding algorithms that
clustered most points into a single cluster. These results suggest that K-means
exhibits greater robustness and stability compared to those of the other
algorithms examined in our study.
3.1.3. RESULTS OF THE ITERATIVE PHASE
ASSESSMENT OF CLUSTERING ALGORITHMS’ SENSITIVITY TO INITIAL SEEDS
We conducted 30 iterations of the K-means and Gaussian algorithms, with varying
starting seeds for different k values. As expected, agglomerative clustering was
not influenced by the starting seed. The results are depicted in Figure A6,
Figure A7 and Figure A8, Appendix A, which present the boxplots of SI, CHI, and
DBI scores.
Although clustering algorithms are acknowledged to be sensitive to initial
seeds, we found minimal variation in performance across different seeds in our
dataset. K-means with k = 2, 3, and 4 exhibited a standard deviation of
performance of less than 0.05 across seeds. However, using Gower’s metric led to
increased variance in certain cases, yielding inferior results in terms of the
DBI score. Hence, our findings suggest that while Gower’s metric can improve
performance and reduce variance in some scenarios, it might increase variance in
others.
EVALUATION OF CLUSTER CONSISTENCY USING RANDOM SUBSETS OF 70% OF THE DATA
In this assessment, involving counting the number of “bad clusters”, K-means,
both with and without the Gower metric, did not generate any bad clusters for k
= 2, 3, and 4. Conversely, agglomerative algorithms using average, single, and
complete linkages consistently generated a high number of bad clusters, as
detailed in Table A1, Appendix A. These findings are supported by the
visualization in Figure A1, where these algorithms clustered most points into a
single cluster, resulting in underrepresented clusters. Using the Gower metric
in K-means, Gaussian mixture, and agglomerative clustering with complete linkage
reduced the number of bad clusters and improved the clustering iterations.
Notably, an increase in the value of k corresponded to a proportional rise in
the number of bad clusters across all algorithms.
COMPARISON OF SI, CHI, AND DBI SCORES USING 100% AND 70% OF DATA
To explore clustering performance, we calculated the SI, CHI, and DBI scores for
each iteration and k value, using a random subset of 70% of the data, and
compared them to the scores obtained when using the complete dataset. The
comparative analysis, presented in Figure A9, Appendix A, reveals consistent
performance, with mean scores showing little variation between using 100% and
70% of the data.
ASSESSING CONSISTENCY WITHIN AND BETWEEN CLUSTERING METHODS USING THE ADJUSTED
RAND INDEX (ARI)
To evaluate consistency within and between clustering algorithms, we conducted
an analysis of intra-algorithm and inter-algorithm similarity. For each
algorithm and k value, we performed clustering on the entire dataset using 10
random seeds, saved the resulting labels, and calculated the ARI score for all
possible pairs of labels, resulting in 45 pairwise comparisons. The results of
the intra-algorithm and inter-algorithm analyses are presented in Table 1.
Table 1. Intra-algorithm and inter-algorithm adjusted Rand index (ARI) scores
using 10 random seeds.
In the intra-algorithm similarity analysis, K-means demonstrated remarkable
robustness, with minimal differences observed for k = 2 and k = 3. The
utilization of Gower’s metric improved the algorithm’s robustness across all k
values. Interestingly, both K-means and Gaussian mixture produced highly similar
clustering results, regardless of whether they used Gower’s distance metric or
not, with ARI scores of 0.944 and 0.978, respectively. As expected, the
agglomerative algorithm was unaffected by different seeds and consistently
yielded identical results, resulting in an intra-score of 1. The ARI scores of
K-means with different metrics were quite similar, particularly for k = 2 and k
= 3 (0.944 and 0.819 respectively). Similarly, Gaussian mixture with different
metrics also achieved a very high score for k = 2 (0.978).
The inter-algorithm similarity analysis revealed a high ARI between K-means and
Gaussian mixture for both metrics. Interestingly, when both algorithms employed
the Gower metric, the ARI increased for k = 4 and k = 5 (0.931 and 0.900
respectively). The agglomerative algorithm with Ward linkage also exhibited a
high ARI score, while the agglomerative algorithm with other linkages
demonstrated lower similarity.
3.2. RESULTS OF THE SSV PHASE
3.2.1. THE SEMANTIC PHASE
SEMANTIC ASSESSMENT OF CLUSTERING METHODS USING 70% OF THE DATA
Following the internal evaluation metrics and ARI score, we conducted a semantic
assessment of the clustering algorithms using subsets comprising 70% of the
data. K-means was chosen due to its superior performance in previous
assessments, evidenced by its CHI and DBI scores, robustness in intra-algorithm
analysis, and similarity to Gaussian mixture and agglomerative (Ward) algorithms
in the inter-algorithm analysis. K-means generated no bad clusters for k = 2, 3,
and 4, but had a few bad clusters for k = 5. Despite the known influence of the
starting seed, we noted minimal variability in the scores across different seed
runs.
To conduct this analysis, we clustered the dataset 10 times using random subsets
of 70% of the data for each k value. In each iteration, we manually relabeled
clusters. We then conducted Z proportion tests to compare demographic and
clinical categorical features between clusters with different semantics.
For k = 2 and k = 3, no statistically significant differences were observed
between any pair of clusters at an alpha level of 0.001, indicating semantic
consistency even with 70% of the data. For k = 4 and k = 5, we found
statistically significant differences in 55 pairs and 2822 pairs, respectively,
at alpha = 0.001.
EXTERNAL SEMANTIC ASSESSMENT USING STATISTICAL AND CLINICAL EVALUATION OF
SELECTED CLUSTERS
Although both k = 2 and k = 3 were viable syntactic solutions for K-means, our
semantic statistical analysis indicated that k = 3 held more clinical
significance. Therefore, we will detail the k = 3 clusters generated by K-means
in the following paragraphs. The results for k = 2 are included in Appendix A
and discussed below.
DEMOGRAPHICS AND SMOKING HABITS ACROSS THE CLUSTERS
The age range was 8–85, the mean age was 44.5 ± 12.4 years, and 1243 (90.7%) of
the participants were women while 127 (9.3%) were men. The demographics and
smoking habits across the clusters are presented in Figure 3.
Figure 3. Demographics and smoking habits across the clusters (k = 3)
(likelihood ratio).
The distribution of the clusters within the study population was as follows:
Cluster 0 (293 subjects, 21.4%), Cluster 1 (632, 46.1%), and Cluster 2 (445,
32.5%) (Figure 3A).
No statistically significant associations were found between any specific
cluster and the following demographic characteristics: age (p = 0.384, Figure
3B), sex (p = 0.228, Figure 3E), being native Israeli (p = 0.793, Figure 3C),
being born in any other immigrant countries (Figure 3D), and marital status
(Figure 3E). However, significant differences were observed among the clusters
in relation to other factors. As depicted in Figure 3E, Cluster 1 reflected the
least severe condition, Cluster 0 reflected the worst, and Cluster 2 fell in
between. The following comparisons showed statistically significant differences
among the clusters: having a steady job (p < 0.001), reporting a worsening of
fibromyalgia in the last year (p < 0.001), and current smoking status (p <
0.001). Cluster 0 had the highest prevalence among those with a high school
education (p = 0.001) and diploma education (postgraduate qualification after
high school, but not an academic degree) (p < 0.001).
COMORBIDITIES AND HISTORY OF TRAUMA ACROSS THE CLUSTERS
The distribution of comorbidities and trauma history across the clusters are
presented in Figure 4. Cluster 0 had a significantly higher prevalence of all
analyzed systemic diseases (Figure 4A), as well as of rheumatological
conditions, except for systemic lupus erythematosus (SLE), where it showed a
significantly lower prevalence (Figure 4B). Additionally, Cluster 0 exhibited a
higher number of emotional and physical traumatic life events both before and
after the onset of fibromyalgia (Figure 4C). There were no statistically
significant differences observed between the three clusters regarding the
presence of certain systemic comorbidities, including malignancy (p = 0.619),
hyperthyroidism (p = 0.194), liver disease (p = 0.086), and kidney disease (p =
0.921). Similarly, no significant differences were found among the clusters for
various comorbid rheumatological conditions, including rheumatoid arthritis (p =
0.209), Sjögren syndrome (p = 0.977), Ankylosing spondylitis (p = 0.155),
psoriatic arthritis (p = 0.073), familial mediterranean fever (p = 0.587),
scleroderma (p = 0.307), gout (p = 0.074), and pseudogout (p = 0.214); these
non-significant findings are not shown in the figures.
Figure 4. Comorbidities and history of trauma across the clusters (likelihood
ratio).
SYMPTOMS, SLEEP AND FUNCTIONAL PROBLEMS AND TREATMENT MODALITIES ACROSS THE
CLUSTERS
The distribution of symptoms, sleep problems, functional mobility problems, and
treatment modalities across the clusters are presented in Figure 5. Cluster 0
exhibited a significantly higher number of symptoms (Figure 5A) along with a
greater prevalence of sleep problems (Figure 5B) and functional mobility issues
(Figure 5C). Regarding treatment modalities, Cluster 0 underwent more treatments
overall, except for exercising (p < 0.001). Notably, no significant differences
were observed in the use of certain treatments, such as the Tai Chi (p = 0.256)
and Feldenkrais method (p = 0.539) (Figure 5D).
Figure 5. Symptoms, sleep and functional problems and treatment modalities
across the clusters (likelihood ratio).
YEARS WITH FIBROMYALGIA, PAIN, SLEEP, QUALITY OF LIFE AND TREATMENT
EFFECTIVENESS ACROSS THE CLUSTERS
The ANOVA analysis and post hoc Bonferroni tests examining the years with
fibromyalgia, pain levels, sleep, quality of life, and treatment effectiveness
across the clusters is presented in Figure 6. The number of years patients had
fibromyalgia did not show any statistically significant differences between the
clusters (p = 0.161). As illustrated in Figure 6A, Cluster 0 represents the most
severe condition, Cluster 1 represents the least severe condition, and Cluster 2
falls in between. Significant differences were observed among the clusters in
terms of pain levels, sleeping hours, sleep quality, and quality of life.
Cluster 0 reported the lowest scores in treatment effectiveness, which were
statistically significantly lower than those of Cluster 1 (p < 0.001), but not
statistically significant compared to those of Cluster 2 (p = 0.319).
Figure 6. Years with fibromyalgia, sleep, quality of life, treatment
effectiveness, and pain level and locations (analysis of variance (ANOVA)
corrected with Bonferroni test for multiple comparisons).
The distribution of specific pain locations across the clusters is depicted in
Figure 6B. Statistically significant differences were observed between the
clusters for all body locations. Contrary to previous observations, the highest
proportions of patients reporting pain were found in Cluster 2, followed by
Cluster 0, which exhibited similar proportions in all painful areas. Cluster 1
had the lowest proportions of patients reporting pain in various body areas.
Notably, none of the patients in Cluster 1 reported pain in all body areas.
In summary, our statistical and clinical evaluation of the k = 3 clusters
indicates that Cluster 0 represents the most severe condition, Cluster 1
represents the least severe condition, and Cluster 2 falls in between.
Significant differences were observed among the clusters in terms of comorbid
medical conditions, symptoms, sleep patterns, functionality, and treatment
outcomes. However, no significant differences were observed in terms of pain
locations.
3.2.2. THE VALIDATION PHASE: A CLUSTER CLASSIFICATION MODEL AND COMPUTATION OF
ITS SHAP VALUES TO ASSESS THE RELATIVE IMPORTANCE OF DIFFERENT FEATURES WHEN
FORMING CLUSTERS
We validated the clustering results using a random forest model to predict
cluster assignments, incorporating aggregated features like medical
comorbidities and treatments. We obtained a mean ROC (receiver operating
characteristic) AUC (area under the curve) score of 0.9943 and an overall
accuracy of 0.9459 with 10-fold cross-validation. To assess the relative
importance of these aggregated features in predicting and interpreting the
clusters, we calculated SHAP values.
Figure 7A displays the top 20 features in the cluster prediction. Dot plots for
cluster 0, 1, and 2 are presented in Figure 7B–D, respectively. Figure 7B shows
that Cluster 0 (sickest) was uniquely positively associated with mobility
functional problems, the most significant feature for this cluster. In contrast,
Cluster 1 (healthiest) and Cluster 2 ranked the sum of painful areas as the most
significant parameter and exhibited a negative association with mobility
problems, as depicted in Figure 7C,D, respectively. While Cluster 0 and 2 were
positively associated with the sum of painful areas, Cluster 1 demonstrated a
negative association. Cluster 0 also had positive associations with several
symptoms, painful areas, comorbidities, sleep problems, mental health, and work
absence, but showed negative associations with quality of life, steady
employment, and sleep quality and duration. Age did not significantly contribute
to cluster differences.
Figure 7. SHAP (Shapley additive explanations) model to predict clusters, k = 3.
(A) Bar plot for mean SHAP values of k = 3; (B) dot plot for Cluster 0; (C) dot
plot for Cluster 1; (D) dot plot for Cluster 2.
3.3. THE K = 2 SOLUTION
The k = 2 solution represented valid syntactic clustering, as determined by the
three-distance metrics used. However, both k = 2 and k = 3 were legitimate
syntactic solutions according to the ARI stability metric. Therefore, we
conducted a semantic statistical analysis to assess the clinical relevance of
the clusters for both k = 2 and k = 3. The k = 3 solution emerged as a
meaningful form of clustering, identifying three sub-classes of fibromyalgia
severity: Cluster 0 (most severe condition), Cluster 1 (least severe condition),
and Cluster 2 (intermediate). Significant differences were observed in various
comparisons related to comorbid medical conditions, symptoms, sleep patterns,
functionality, and treatment outcomes, although not in terms of pain locations.
To evaluate the k = 2 solution, we employed the same statistical tests, ML model
(random forest), and SHAP explanations. Detailed results for the k = 2 solution
are available in the Appendix A. k = 2 clustering resulted in two clusters:
Cluster 0 with 731 subjects and Cluster 1 with 639 subjects. Cluster 0 consisted
of patients with more severe conditions, while Cluster 1 comprised patients with
less severe conditions. Further analysis showed that Cluster 0 in the k = 2
solution combined elements of both Cluster 0 (most severe) and Cluster 2
(intermediate severity) from the k = 3 solution. Patients in Cluster 1 for the k
= 3 solution predominantly remained as Cluster 1 (healthier cluster) in the k =
2 solution.
To assess differences within each feature between the two clusters, we used
Pearson’s chi-square test for categorical parameters and an independent t-test
for continuous variables. The results of these tests are detailed in Table A2 in
Appendix A. Although there were differences between the two clusters in the k =
2 solution, the k = 3 solution exhibited a greater number of statistically and
clinically significant features. The absence of significant differences in
certain features could be attributed to the merging of the most severe and
intermediate clusters.
In the prediction models for k = 2 using the random forest model with 10-fold
cross-validation, we achieved an ROC AUC and accuracy of 0.99. The SHAP
algorithm results for K-means clustering with k = 2 are presented in Appendix A
(Figure A10). Pain locations and mobility functional problems were highly ranked
in both Cluster 0 and Cluster 1, but with opposite associations.
Both k = 2 and k = 3 partitioning options using K-means are valid clustering
solutions. However, the k = 3 solution holds greater clinical significance and
may contribute to a better understanding of the underlying mechanisms of
fibromyalgia, potentially leading to more effective therapeutic interventions.
Therefore, both solutions are presented in the results of our study.
To better understand the nuanced differences and key characteristics that
distinguish the k = 2 and k = 3 clustering solutions, we included Figure 8. This
figure displays the cluster visualizations as defined by the k-means algorithm
for both k = 2 and k = 3 scenarios, using the first two PCA components. This
approach provides a more instinctive comprehension of the clusters’ structure
and the critical factors differentiating them. Additionally, the figure includes
bar plot graphs that highlight the top five influential features for each
cluster, as identified through our SHAP analysis. These bar plots provide
insights into the defining characteristics of each patient group, thereby
enhancing our understanding of each cluster in the context of fibromyalgia.
Figure 8. Comparative visualization of k = 2 and k = 3 clustering solutions in
fibromyalgia patient analysis. The top-left panel displays k = 2 clustering
using PCA components, clearly delineating Clusters 0 and 1. The top-right panel
presents k = 3 clustering, offering a detailed view of Clusters 0, 1, and 2. The
bottom panel includes bar plots that highlight the five most significant
attributes for each cluster, with the left side pertaining to the k = 2 solution
and the right side pertaining to the k = 3 solution.
4. DISCUSSION
The present study introduces the CDI-SSV methodology, a novel multi-dimensional
approach to discover and validate the optimal number of clusters. Unlike
traditional clustering approaches that often rely on a single algorithm or
metric, our method uniquely integrates several clustering algorithms, distance
measures, and bagging and clustering iterations (the CDI phase), followed by the
SSV phase, computing statistical differences among clusters for several
meaningful additional clinical semantic features. Finally, to validate our
results, we generated a machine learning model that classified the patients into
clusters and assessed the importance of the demographic and clinical features,
using SHAP values.
A key innovation of our study is the application of this multi-dimensional
approach to a large cohort of 1370 fibromyalgia patients, a scale significantly
larger than that of most previous studies in this domain. This extensive sample
size allows for the capturing of a broader spectrum of patient variability,
thereby enhancing the reliability and applicability of our findings.
To the best of our knowledge, this is the first study published that employs
such a holistic and multi-dimension methodology in a medical context,
demonstrated here with fibromyalgia patients. The integration of multiple
clustering algorithms alongside both syntactic and semantic validation
techniques sets our approach apart from existing methods. Furthermore, the
incorporation of SHAP values in the validation process not only provides a
deeper understanding of the influence of demographic and clinical features on
cluster formation but also highlights the potential of our methodology in the
realm of personalized medicine.
We suggest that the CDI-SSV methodology can be effectively applied in across
various medical domains for clustering analysis to identify patient sub-groups.
Its capability to handle large datasets and integrate multiple data dimensions
makes it a versatile tool for uncovering meaningful patterns in complex medical
data. This approach has the potential to significantly contribute to the
advancement of patient stratification and personalized treatment strategies,
extending well beyond the scope of fibromyalgia to other medical conditions.
The present study found K-means to be a more robust and stable clustering method
compared to other algorithms tested. This was noted due to several findings.
First, our results, presented in Figure 2, indicated that K-means outperformed
other algorithms in two out of three evaluation metrics (CHI and the DBI
scores). K-means with Gower’s distance metric also had the best SI score for k =
2, 3, and 5. Moreover, we conducted 30 iterations of K-means and Gaussian
mixture clustering algorithms with different seeds to assess their average
performance. Figure A6, Figure A7 and Figure A8 in Appendix A show the results.
Our findings demonstrate that the variation in performance across different
seeds was minimal, especially for K-means with k = 2 and 3, in our dataset.
Furthermore, we conducted 30 iterations using randomly bagged subsets (selected
with replacement) comprising 70% of the data, and the results presented in Table
A1 demonstrate that K-means did not create “bad” clusters, defined as clusters
with less than 5% of the data, for k = 2, 3, or 4 with or without using the
Gower distance metric. The mean SI, CHI, and DBI scores did not vary
significantly when using 100% compared to 70% of the data (Figure A9). The
assessment of the consistency within and between clustering methods using the
adjusted Rand index (ARI) revealed that again, K-means was found to be a very
robust algorithm, which was able to cluster individuals similarly to Gaussian
mixture and agglomerative (Ward) algorithms with almost no difference in the ARI
for k = 2 and k = 3. Based on the best overall performance of K-means according
to all these assessments, we chose K-means as the preferred method and performed
a semantic assessment of clustering methods using 70% of the data. No
statistically significant differences were found, across all 151 categorical
features, between any pair of equivalent clusters for k = 2 and k = 3 at a
significance level of alpha = 0.001. Considering that both K = 2 and k = 3 were
legitimate syntactic solutions, we further performed statistical analysis and
evaluated the clinical relevance of the created clusters. While a cluster number
of k = 2 yielded better syntactic performance in the SI, CHI, and DBI scores,
the ARI scores of k = 2 were similar to those of k = 3, suggesting that even
with a larger number of clusters, stability is maintained with respect to the
same pairs of patients appearing in the same cluster. Even more importantly, k =
3 partitioning seemed to represent a more clinically meaningful partition, since
the three clusters’ solution better explained the clinical picture presented by
fibromyalgia patients, which seems to be composed of low-, intermediate-, and
high-grade severity patients. Compared to the k = 2 solution, the k = 3 solution
manifested more statistically significant differences in all comparisons among
clusters in terms of comorbid medical conditions, symptoms, sleep patterns,
functionality, and treatment outcomes, but not in terms of pain locations.
A recent study by Fernández-de-las-Peñas et al. [10] also found differences
between subgroups of fibromyalgia patients in terms of psychological, cognitive,
health-related, and physical features but similar widespread pressure pain
sensitivity. However, their study, which identified only two subgroups, had a
smaller population size (113) compared to that of our study (1370) and included
only women. Additionally, their methodology differed from ours, as we employed a
detailed CDI method to assess clustering. Finally, in our study, the sickest
cluster was the smallest, representing 21.4% of the population, which may be
challenging to capture in smaller cohorts.
Widespread pain is the hallmark of fibromyalgia, and therefore may not
discriminate well between fibromyalgia patients. Fibromyalgia is now thought to
be a pain regulation disorder, often classified as central sensitization [6],
due to alterations in central nervous system pain and sensory processing [7]. We
found differences between clusters not only in subjective parameters, but also
in objective parameters, such as the presence of systemic and rheumatological
comorbidities, symptoms, and functional problems such as using a walking stick
or a wheelchair, which indicates a more serious clinical condition. These
comorbid conditions may also contribute to pain. Therefore, the clinical
implications of identifying these subgroups could imply different underlying
mechanisms in each of these subgroups, a hypothesis that should be studied in
future research.
Finally, to validate our clustering results using a supervised classification
methodology, our random forest model accurately classified patients into three
clusters with an AUC of 0.994 and accuracy of 0.946. Then, by computing the
model’s SHAP values, we identified a distinct profile that enabled the model to
classify the patients into each cluster. In particular, Cluster 0, the sickest
cluster, is characterized by mobility functional problems, accompanying
symptoms, painful areas, comorbidities, sleep and mental health problems,
absenteeism, a lower quality of life, and treatment effectiveness
self-assessment. These features serve as markers for evidence-based personalized
diagnosis and might suggest that this subgroup requires different management
strategies, providing clinical application points to patient-centered treatment.
The identification of three distinct fibromyalgia patient profiles in our study,
as shown in Figure 8, has important implications for clinical management. These
profiles enable more personalized treatment strategies, allowing clinicians to
tailor interventions to each subgroup’s severity and characteristics. For
example, the most severely affected cluster may require aggressive,
multidisciplinary treatment, while others could benefit from less intensive
therapies focused on lifestyle and symptom management. These findings also
inform future research into fibromyalgia’s pathophysiology, particularly in
understanding different patterns of central sensitization across subgroups. This
knowledge is crucial for developing targeted therapies. Applying our CDI-SSV
methodology in clinical practice can facilitate the early identification of
patient subgroups, leading to earlier, more effective interventions and
potentially better long-term outcomes. Ultimately, our study’s insights could
significantly refine fibromyalgia diagnosis, management, and treatment, aligning
with personalized medicine principles and improving patient care.
Strength and limitations: The main contributions of the study include [1] a
novel, highly general, multidimensional clustering methodology, CDI-SSV, for
identifying patient subgroups; [2] the application of the CDI-SSV methodology to
a dataset of fibromyalgia patients, which demonstrated its effectiveness in
uncovering three distinct patient profiles, enabling a more nuanced
understanding of fibromyalgia based on demographic and clinical features, and
providing a potential to improve clinical care. The provision of clinical
markers for evidence-based personalized diagnosis, management, and prognosis
enables a more personalized tailoring of treatments and interventions.
Regarding limitations, although this study analyzed important features, it would
also be useful to obtain genetic and laboratory results, thus enabling us to
better understand the clinical significance of the different clusters.
5. CONCLUSIONS
In conclusion, our study highlights the value of the CDI-SSV methodology in
clustering and classifying fibromyalgia patients, demonstrating its potential
applicability beyond fibromyalgia to other medical domains. This methodology
facilitates enhanced patient stratification, paving the way for improved
clinical outcomes across various conditions. The identification of distinct
profiles within fibromyalgia patients allows for a more targeted and
personalized approach in diagnosis, management, and prognosis. The practical
implications of these findings, including the potential for more effective and
patient-centric treatment strategies, underscore the significance of our work in
advancing the understanding and care of fibromyalgia. Ultimately, this work
contributes to the evolving field of personalized medicine, offering data-driven
insights and evidence-based practices that can transform patient care.
AUTHOR CONTRIBUTIONS
Conceptualization, A.G., Y.S. and G.A.; Methodology, A.G., Y.S. and G.A.;
Software, A.G., Y.S., M.W.R. and G.A.; Validation, A.G., Y.S., M.W.R. and G.A.;
Formal analysis, A.G., Y.S., M.W.R., C.G. and G.A.; Investigation, A.G., Y.S.,
H.P., E.B.-C., A.B.-Y., E.S., C.G., S.S.S. and G.A.; Data curation, E.S. and
S.S.S.; Writing—original draft, G.A.; Writing—review & editing, A.G., Y.S.,
M.W.R., H.P., E.B.-C., A.B.-Y., E.S., C.G. and S.S.S.; Visualization, A.G.,
Y.S., M.W.R. and G.A.; Supervision, G.A. All authors have read and agreed to the
published version of the manuscript.
FUNDING
This research was funded by the Israeli Ministry of Innovation, Science and
Technology, grant number 3-15608.
INSTITUTIONAL REVIEW BOARD STATEMENT
The study was conducted in accordance with the Declaration of Helsinki and
approved by the Institutional Review Board of Hadassah Medical Organization
(HMO) (protocol code: 0205-19-HMO date of approval: 2019).
INFORMED CONSENT STATEMENT
Patient consent was waived due to the fact this study involved only anonymous
survey analysis.
DATA AVAILABILITY STATEMENT
Data are contained within the article and Appendix A.
ACKNOWLEDGMENTS
The authors would like to acknowledge the Israeli Society for Fibromyalgia and
Chronic Fatigue syndrome (Asaf association) as well as the Israeli association
for patients with rheumatic, autoimmune or inflammatory diseases (Inbar
association) for spreading the online research to their members.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
APPENDIX A. SUPPLEMENTARY ANALYSES
Figure A1. Visualization of different algorithms for different values of k. Each
color represents a different cluster. The figure illustrates the clustering
results obtained using different algorithms and values of k. Notably, the
K-means and Gaussian clustering methods demonstrate a higher degree of
similarity in their cluster assignments compared to the agglomerative clustering
method utilizing the Ward linkage criterion. Also, it can be noted that when
alternative linkage criteria were employed with the agglomerative clustering
method, the majority of data points were assigned to a single cluster. This
observation suggests that utilizing linkage criteria other than Ward may lead to
less meaningful cluster assignments.
Figure A1. Visualization of different algorithms for different values of k. Each
color represents a different cluster. The figure illustrates the clustering
results obtained using different algorithms and values of k. Notably, the
K-means and Gaussian clustering methods demonstrate a higher degree of
similarity in their cluster assignments compared to the agglomerative clustering
method utilizing the Ward linkage criterion. Also, it can be noted that when
alternative linkage criteria were employed with the agglomerative clustering
method, the majority of data points were assigned to a single cluster. This
observation suggests that utilizing linkage criteria other than Ward may lead to
less meaningful cluster assignments.
To further investigate the impact of the choice of linkage criterion on the
results of agglomerative clustering, we present in Figure A2 a dendrogram of the
hierarchical clustering algorithm using different linkage criteria. As can be
observed from the dendrogram, the choice of linkage criterion has a significant
impact on the results of agglomerative clustering. Different linkage criteria
can produce different cluster assignments for the same data set. This reinforces
our previous conclusion that using linkage criteria other than Ward’s may result
in less meaningful cluster assignments and highlights the importance of
carefully selecting an appropriate linkage criterion when using agglomerative
clustering.
Figure A2. Dendrograms of hierarchical clustering using different linkage
criteria. Each vertical line represents a merge between clusters. The height of
the vertical lines represents the distance (or dissimilarity) at which clusters
are merged. The colors signify different clusters formed, based on the standard
threshold (70% of the maximum linkage distance).
Figure A2. Dendrograms of hierarchical clustering using different linkage
criteria. Each vertical line represents a merge between clusters. The height of
the vertical lines represents the distance (or dissimilarity) at which clusters
are merged. The colors signify different clusters formed, based on the standard
threshold (70% of the maximum linkage distance).
Figure A3, Figure A4 and Figure A5 present the scores of the different
algorithms, for the different values of k, for each of the SI, CHI, and DBI
scores respectively.
Figure A3. The SI scores of each algorithm in the different values of k. The
agglomerative algorithm with complete, average and single linkage had the best
scores for every k. The agglomerative algorithm with ward linkage was the worst
for almost every k (except k = 5). K-means and Gaussian mixture have very
similar scores, both when using Gower’s distance metric and when not using it.
Figure A3. The SI scores of each algorithm in the different values of k. The
agglomerative algorithm with complete, average and single linkage had the best
scores for every k. The agglomerative algorithm with ward linkage was the worst
for almost every k (except k = 5). K-means and Gaussian mixture have very
similar scores, both when using Gower’s distance metric and when not using it.
Figure A4. The CHI score of K-means was superior for every k, followed by
Gaussian mixture and the agglomerative algorithm with ward linkage.
Figure A4. The CHI score of K-means was superior for every k, followed by
Gaussian mixture and the agglomerative algorithm with ward linkage.
Figure A5. On the left, the DBI scores of all algorithms; on the right, after
the removal of the three worst (highest) ones. The agglomerative algorithm with
complete, average, and single linkage had the lowest (best) results for all ks.
K-means performed better than Gaussian mixture did for all ks. Gaussian mixture
performed better than did the agglomerative algorithm with Ward linkage for k =
2 and 3 but not for k = 4 and 5.
Figure A5. On the left, the DBI scores of all algorithms; on the right, after
the removal of the three worst (highest) ones. The agglomerative algorithm with
complete, average, and single linkage had the lowest (best) results for all ks.
K-means performed better than Gaussian mixture did for all ks. Gaussian mixture
performed better than did the agglomerative algorithm with Ward linkage for k =
2 and 3 but not for k = 4 and 5.
Figure A6, Figure A7 and Figure A8: Distribution of SI, CHI, and DBI scores for
K-means and Gaussian mixture with and without Gower’s distance metric for the
different values of k, sampling 100% of the data, when using different starting
seeds.
Figure A6. Distribution of SI scores for K-means and Gaussian with and without
Gower’s distance metric for the different values of k, sampling 100% of the
data, when using different starting seeds.
Figure A6. Distribution of SI scores for K-means and Gaussian with and without
Gower’s distance metric for the different values of k, sampling 100% of the
data, when using different starting seeds.
For the SI score, Figure A6 shows that using the Gower distance metric in
K-means clustering improved performance for all values of k (as was shown in
Semantic Assessment of Clustering Methods using 70% of the Data) and reduced the
variance in performance across seeds. For Gaussian mixture, using Gower’s
distance metric reduced variance only for k = 2.
Figure A7. Distribution of CHI scores for K-means and Gaussian mixture with and
without Gower’s distance metric for the different values of k, sampling 100% of
the data, when using different starting seeds.
Figure A7. Distribution of CHI scores for K-means and Gaussian mixture with and
without Gower’s distance metric for the different values of k, sampling 100% of
the data, when using different starting seeds.
For the CHI score Figure A7 indicates that using the Gower distance metric in
K-means clustering decreased the average performance for all values of k but
only reduced the variance in performance for k = 2 and k = 5.
Figure A8. Distribution of DBI scores for K-means and Gaussian mixture with and
without Gower’s distance metric for the different values of k, sampling 100% of
the data, when using different starting seeds.
Figure A8. Distribution of DBI scores for K-means and Gaussian mixture with and
without Gower’s distance metric for the different values of k, sampling 100% of
the data, when using different starting seeds.
For the BDI score, Figure A8 shows that using the Gower distance metric
increased variance and produced worse results.
Table A1. Number of bad clustering defined as clusters containing less than 5%
of data by each algorithm for each k, when using random subsets of 70% of the
data.
Table A1. Number of bad clustering defined as clusters containing less than 5%
of data by each algorithm for each k, when using random subsets of 70% of the
data.
Bad ClusteringsK = 2K = 3K = 4K = 5K-means00018K-means
Gower0005Gaussian231316Gaussian Gower0014Agglomerative Ward051634Agglomerative
average306090120Agglomerative average Gower306090120Agglomerative
single306090120Agglomerative single Gower306090120Agglomerative complete29
5180109Agglomerative complete Gower1162847
Figure A9. Comparison of silhouette index (SI), Calinski–Harabasz index (CHI),
and Davies–Bouldin index (DBI) scores using 100% (left) and 70% (right) of the
data. The figure shows consistent performance between 70% and 100% of the data,
as mean scores did not significantly vary between them.
Figure A9. Comparison of silhouette index (SI), Calinski–Harabasz index (CHI),
and Davies–Bouldin index (DBI) scores using 100% (left) and 70% (right) of the
data. The figure shows consistent performance between 70% and 100% of the data,
as mean scores did not significantly vary between them.
Table A2. Pearson’s chi-square (2) test for k = 2 was used to determine whether
or not the distributions of the two clusters differed significantly within each
categorical feature.
Table A2. Pearson’s chi-square (2) test for k = 2 was used to determine whether
or not the distributions of the two clusters differed significantly within each
categorical feature.
FeaturesVariableCluster_0Cluster_1p ValuesSexYes656 (89.74)587 (91.86)0.1766
No75 (10.26)52 (8.14) Other rheumatic dis.Yes123 (16.83)63 (9.86)<0.001 No608
(83.17)576 (90.14) Behcet’s diseaseYes15 (2.05)3 (0.47)0.0103 No716 (97.95)636
(99.53) FMFYes25 (3.42)16 (2.5)0.3208 No706 (96.58)623 (97.5) Inflammatory
muscle diseaseYes25 (3.42)7 (1.1)0.0045 No706 (96.58)632 (98.9) VasculitisYes4
(0.55)7 (1.1)0.2566 No727 (99.45)632 (98.9) OsteoarthritisYes36 (4.92)27
(4.23)0.5375 No695 (95.08)612 (95.77) SclerodermaYes1 (0.14)2 (0.31)0.4864 No730
(99.86)637 (99.69) PseudogoutYes1 (0.14)0 (0.0)0.3496 No730 (99.86)639 (100.0)
GoutYes6 (0.82)7 (1.1)0.6009 No725 (99.18)632 (98.9) SLEYes22 (3.01)20
(3.13)0.8975 No709 (96.99)619 (96.87) Sjogren’sYes12 (1.64)11 (1.72)0.9086 No719
(98.36)628 (98.28) Arthritis related to IBDYes11 (1.5)9 (1.41)0.8821 No720
(98.5)630 (98.59) SpondyloarthritisYes7 (0.96)4 (0.63)0.4926 No724 (99.04)635
(99.37) Ankylosing spondylitisYes10 (1.37)11 (1.72)0.5953 No721 (98.63)628
(98.28) Psoriatic arthritisYes36 (4.92)20 (3.13)0.0942 No695 (95.08)619 (96.87)
RAYes58 (7.93)44 (6.89)0.4608 No673 (92.07)595 (93.11) No other rheumatological
conditionYes443 (60.6)429 (67.14)0.0121 No288 (39.4)210 (32.86)
EndometriosisYes50 (6.84)20 (3.13)0.0019 No681 (93.16)619 (96.87) Chronic
sinusitisYes66 (9.03)47 (7.36)0.2613 No665 (90.97)592 (92.64) AsthmaYes78
(10.67)43 (6.73)0.0103 No653 (89.33)596 (93.27) AllergyYes173 (23.67)108
(16.9)0.002 No558 (76.33)531 (83.1) Liver diseaseYes16 (2.19)15 (2.35)0.8439
No715 (97.81)624 (97.65) ObesityYes181 (24.76)140 (21.91)0.2139 No550 (75.24)499
(78.09) UveitisYes41 (5.61)22 (3.44)0.0562 No690 (94.39)617 (96.56) AnemiaYes114
(15.6)73 (11.42)0.0249 No617 (84.4)566 (88.58) Obstructive sleep apneaYes62
(8.48)29 (4.54)0.0035 No669 (91.52)610 (95.46) Renal diseaseYes9 (1.23)8
(1.25)0.9724 No722 (98.77)631 (98.75) Chronic headacheYes283 (38.71)163
(25.51)<0.001 No448 (61.29)476 (74.49) Psychiatric conditionsYes171 (23.39)100
(15.65)<0.001 No560 (76.61)539 (84.35) Endocrinological conditionsYes55 (7.52)35
(5.48)0.1272 No676 (92.48)604 (94.52) HyperthyroidismYes21 (2.87)11 (1.72)0.1593
No710 (97.13)628 (98.28) HypothyroidismYes70 (9.58)83 (12.99)0.0454 No661
(90.42)556 (87.01) Dermatological conditionsYes68 (9.3)45 (7.04)0.1293 No663
(90.7)594 (92.96) MalignancyYes7 (0.96)7 (1.1)0.8002 No724 (99.04)632 (98.9)
Irritable bowel syndromeYes229 (31.33)163 (25.51)0.0174 No502 (68.67)476 (74.49)
Inflammatory bowel diseaseYes37 (5.06)25 (3.91)0.3073 No694 (94.94)614 (96.09)
Peptic ulcersYes69 (9.44)29 (4.54)<0.001 No662 (90.56)610 (95.46) Pulmonary
diseaseYes29 (3.97)10 (1.56)0.0076 No702 (96.03)629 (98.44) HyperlipidemiaYes94
(12.86)85 (13.3)0.8083 No637 (87.14)554 (86.7) HypertensionYes91 (12.45)77
(12.05)0.8224 No640 (87.55)562 (87.95) Cardiovascular diseasesYes25 (3.42)10
(1.56)0.0299 No706 (96.58)629 (98.44) DiabetesYes60 (8.21)34 (5.32)0.035 No671
(91.79)605 (94.68) Having a steady jobYes378 (51.71)419 (65.57)<0.001 No353
(48.29)220 (34.43) Fibromyalgia had worsened in the past yearYes633 (86.59)468
(73.24)<0.001 No98 (13.41)171 (26.76) Emotional trauma before fibromyalgia
onsetYes428 (58.55)373 (58.37)0.947 No303 (41.45)266 (41.63) Physical trauma
before fibromyalgia onsetYes279 (38.17)201 (31.46)0.0094 No452 (61.83)438
(68.54) Emotional trauma after fibromyalgia onsetYes383 (52.39)282 (44.13)0.0023
No348 (47.61)357 (55.87) Physical trauma after fibromyalgia onsetYes162
(22.16)81 (12.68)<0.001 No569 (77.84)558 (87.32) Pain causing awakening from
sleepYes651 (89.06)479 (74.96)<0.001 No80 (10.94)160 (25.04) Waking up
tiredYes713 (97.54)605 (94.68)0.0057 No18 (2.46)34 (5.32) Other sleep
problemsYes90 (12.31)40 (6.26)<0.001 No641 (87.69)599 (93.74) Sleeping
medicationYes201 (27.5)142 (22.22)0.0246 No530 (72.5)497 (77.78) Bad
dreamsYes208 (28.45)105 (16.43)<0.001 No523 (71.55)534 (83.57)
Snoring/coughingYes157 (21.48)81 (12.68)<0.001 No574 (78.52)558 (87.32) Waking
up in the middle of the night/early morningYes566 (77.43)413 (64.63)<0.001 No165
(22.57)226 (35.37) Inability to breathe comfortablyYes137 (18.74)61 (9.55)<0.001
No594 (81.26)578 (90.45) Grinding/clenching teethYes226 (30.92)180 (28.17)0.2666
No505 (69.08)459 (71.83) Problems maintaining sleepYes596 (81.53)417
(65.26)<0.001 No135 (18.47)222 (34.74) Problems falling asleepYes470 (64.3)289
(45.23)<0.001 No261 (35.7)350 (54.77) Left footYes721 (98.63)340 (53.21)<0.001
No10 (1.37)299 (46.79) Right footYes720 (98.5)335 (52.43)<0.001 No11 (1.5)304
(47.57) Lower leg: leftYes706 (96.58)173 (27.07)<0.001 No25 (3.42)466 (72.93)
Lower leg: rightYes701 (95.9)169 (26.45)<0.001 No30 (4.1)470 (73.55) Upper leg:
left Yes709 (96.99)244 (38.18)<0.001 No22 (3.01)395 (61.82) Upper leg:
rightYes698 (95.49)253 (39.59)<0.001 No33 (4.51)386 (60.41) Left buttock Yes688
(94.12)165 (25.82)<0.001 No43 (5.88)474 (74.18) Right buttock Yes696 (95.21)160
(25.04)<0.001 No35 (4.79)479 (74.96) PelvisYes716 (97.95)330 (51.64)<0.001 No15
(2.05)309 (48.36) Lower backYes726 (99.32)474 (74.18)<0.001 No5 (0.68)165
(25.82) Upper backYes714 (97.67)341 (53.36)<0.001 No17 (2.33)298 (46.64)
AbdomenYes689 (94.25)204 (31.92)<0.001 No42 (5.75)435 (68.08) ChestYes684
(93.57)122 (19.09)<0.001 No47 (6.43)517 (80.91) Left wristYes722 (98.77)303
(47.42)<0.001 No9 (1.23)336 (52.58) Right wristYes722 (98.77)354 (55.4)<0.001
No9 (1.23)285 (44.6) Lower arm: leftYes718 (98.22)208 (32.55)<0.001 No13
(1.78)431 (67.45) Lower arm: rightYes713 (97.54)220 (34.43)<0.001 No18 (2.46)419
(65.57) Upper arm: leftYes697 (95.35)102 (15.96)<0.001 No34 (4.65)537 (84.04)
Upper arm: rightYes695 (95.08)113 (17.68)<0.001 No36 (4.92)526 (82.32) Left
shoulderYes722 (98.77)334 (52.27)<0.001 No9 (1.23)305 (47.73) Right
shoulderYes716 (97.95)327 (51.17)<0.001 No15 (2.05)312 (48.83) Left jawYes678
(92.75)141 (22.07)<0.001 No53 (7.25)498 (77.93) Right jawYes683 (93.43)144
(22.54)<0.001 No48 (6.57)495 (77.46) Left headYes695 (95.08)163 (25.51)<0.001
No36 (4.92)476 (74.49) Right headYes699 (95.62)161 (25.2)<0.001 No32 (4.38)478
(74.8) NeckYes727 (99.45)446 (69.8)<0.001 No4 (0.55)193 (30.2) All bodyYes629
(86.05)0 (0.0)<0.001 No102 (13.95)639 (100.0) DizzinessYes501 (68.54)280
(43.82)<0.001 No230 (31.46)359 (56.18) Nausea/vomitingYes336 (45.96)172
(26.92)<0.001 No395 (54.04)467 (73.08) Abdominal painYes467 (63.89)301
(47.1)<0.001 No264 (36.11)338 (52.9) ConstipationYes334 (45.69)200 (31.3)<0.001
No397 (54.31)439 (68.7) HeartburnYes297 (40.63)179 (28.01)<0.001 No434
(59.37)460 (71.99) Taste disorderYes169 (23.12)38 (5.95)<0.001 No562 (76.88)601
(94.05) Smell disorderYes198 (27.09)69 (10.8)<0.001 No533 (72.91)570 (89.2)
XerostomiaYes403 (55.13)221 (34.59)<0.001 No328 (44.87)418 (65.41)
EpistaxisYes84 (11.49)46 (7.2)0.0068 No647 (88.51)593 (92.8) Frequent urges to
urinateYes364 (49.79)212 (33.18)<0.001 No367 (50.21)427 (66.82) TinnitusYes343
(46.92)198 (30.99)<0.001
Figure A10. SHAP (SHapley Additive exPlanations) Model to predict clusters for K
= 2.
Figure A10. SHAP (SHapley Additive exPlanations) Model to predict clusters for K
= 2.
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Figure 1. The CDI-SSV Methodology: An Integrated Approach for Clustering
Validation. The figure provides an overview of the Clustering, Distance
measures, and Iterative Statistical and Semantic Validation (CDI-SSV)
methodology. The CDI phase serves as the initial step, involving the evaluation
of cluster quality, the impact of different starting seeds, and the consistency
of clusters across various algorithms and pre-defined values of k. Within- and
between-consistency checks, along with evaluations of internal semantic
consistency, are performed to assess the optimal algorithm and values of k. In
the subsequent SSV phase, an external semantic analysis of the results is
conducted, with a particular focus on the clinical context, thus enhancing the
validation process. Finally, machine learning techniques are employed to
validate the results, and their interpretation is facilitated by SHAP (Shapley
additive explanations) values.
Figure 2. Evaluation of clustering algorithms using evaluation metrics. (A)
Silhouette index (SI): the SI scores for different values of k indicate that
K-means with Gower’s distance metric achieved the highest score for k = 2, 3,
and 5. (B) Calinski–Harabasz index (CHI): K-means consistently outperformed
other algorithms, achieving the best score across all values of k. (C)
Davies–Bouldin index (DBI): K-means demonstrated superior results for all values
of k.
Figure 3. Demographics and smoking habits across the clusters (k = 3)
(likelihood ratio).
Figure 4. Comorbidities and history of trauma across the clusters (likelihood
ratio).
Figure 5. Symptoms, sleep and functional problems and treatment modalities
across the clusters (likelihood ratio).
Figure 6. Years with fibromyalgia, sleep, quality of life, treatment
effectiveness, and pain level and locations (analysis of variance (ANOVA)
corrected with Bonferroni test for multiple comparisons).
Figure 7. SHAP (Shapley additive explanations) model to predict clusters, k = 3.
(A) Bar plot for mean SHAP values of k = 3; (B) dot plot for Cluster 0; (C) dot
plot for Cluster 1; (D) dot plot for Cluster 2.
Figure 8. Comparative visualization of k = 2 and k = 3 clustering solutions in
fibromyalgia patient analysis. The top-left panel displays k = 2 clustering
using PCA components, clearly delineating Clusters 0 and 1. The top-right panel
presents k = 3 clustering, offering a detailed view of Clusters 0, 1, and 2. The
bottom panel includes bar plots that highlight the five most significant
attributes for each cluster, with the left side pertaining to the k = 2 solution
and the right side pertaining to the k = 3 solution.
Table 1. Intra-algorithm and inter-algorithm adjusted Rand index (ARI) scores
using 10 random seeds.
Intra-Algorithm adj. Rand Indexk = 2k = 3K = 4K = 5Intra-Algorithm adj. Rand
Indexk = 2k = 3k = 4k = 5K-means0.9980.9770.8930.722K-means,
Gaussian0.7510.6150.5900.574Gaussian0.9900.7470.6730.597K-means Gower, Gaussian
Gower0.7640.7430.9310.900K-means Gower10.99910.985K-means Gower,
Gaussian0.7560.5370.5610.525Gaussian Gower10.73908820.80K-means, Gaussian
Gower0.7510.6740.62390.543Gaussian X Gaussian Gower0.9780.6090.5580.518AggWard,
K-means0.7640.7430.930.9007K-means X K-means Gower 0.9440.8190.6420.546AggWard,
gaussian0.5200.3670.330.297AggWard1111AggComplete,
K-means−0.0001−0.00020.00130.0068AggComplete1111Agg Complete,
Gaussian0.000050.00050.00450.0106AggAverage1111Agg Average,
K-means−0.00010.00200.00080.0014AggComplete Gower1111Agg Average,
Gaussian0.000050.00180.00250.0018AggAvg Gower1111Agg Single,
K-means−0.0001−0.00020.00050.0013
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Goldstein, A.; Shahar, Y.; Weisman Raymond, M.; Peleg, H.; Ben-Chetrit, E.;
Ben-Yehuda, A.; Shalom, E.; Goldstein, C.; Shiloh, S.S.; Almoznino, G.
Multi-Dimensional Validation of the Integration of Syntactic and Semantic
Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor
Big Data Study. Bioengineering 2024, 11, 97.
https://doi.org/10.3390/bioengineering11010097
AMA Style
Goldstein A, Shahar Y, Weisman Raymond M, Peleg H, Ben-Chetrit E, Ben-Yehuda A,
Shalom E, Goldstein C, Shiloh SS, Almoznino G. Multi-Dimensional Validation of
the Integration of Syntactic and Semantic Distance Measures for Clustering
Fibromyalgia Patients in the Rheumatic Monitor Big Data Study. Bioengineering.
2024; 11(1):97. https://doi.org/10.3390/bioengineering11010097
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and Galit Almoznino. 2024. "Multi-Dimensional Validation of the Integration of
Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in
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Goldstein, A.; Shahar, Y.; Weisman Raymond, M.; Peleg, H.; Ben-Chetrit, E.;
Ben-Yehuda, A.; Shalom, E.; Goldstein, C.; Shiloh, S.S.; Almoznino, G.
Multi-Dimensional Validation of the Integration of Syntactic and Semantic
Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor
Big Data Study. Bioengineering 2024, 11, 97.
https://doi.org/10.3390/bioengineering11010097
AMA Style
Goldstein A, Shahar Y, Weisman Raymond M, Peleg H, Ben-Chetrit E, Ben-Yehuda A,
Shalom E, Goldstein C, Shiloh SS, Almoznino G. Multi-Dimensional Validation of
the Integration of Syntactic and Semantic Distance Measures for Clustering
Fibromyalgia Patients in the Rheumatic Monitor Big Data Study. Bioengineering.
2024; 11(1):97. https://doi.org/10.3390/bioengineering11010097
Chicago/Turabian Style
Goldstein, Ayelet, Yuval Shahar, Michal Weisman Raymond, Hagit Peleg, Eldad
Ben-Chetrit, Arie Ben-Yehuda, Erez Shalom, Chen Goldstein, Shmuel Shay Shiloh,
and Galit Almoznino. 2024. "Multi-Dimensional Validation of the Integration of
Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in
the Rheumatic Monitor Big Data Study" Bioengineering 11, no. 1: 97.
https://doi.org/10.3390/bioengineering11010097
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