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 * George B. Moody PhysioNet Challenge 2024: Challenge Update - Partnering with
   Data Science Africa and
 * Responsible use of MIMIC data with online services like GPT


PHYSIONET

The Research Resource for Complex Physiologic Signals


Data Software Challenges Tutorials



FEATURED RESOURCES


Software Open Access


SOFTWARE FOR COMPUTING HEART RATE FRAGMENTATION

Madalena Costa

Heart rate fragmentation: a new method for the analysis of cardiac interbeat
interval time series. The code provided can be run in Windows, Mac and Linux
machines.

heart rate variability aging cardiovascular disease vagal tone time series
analysis prediction of atrial fibrillation cardiac autonomic function prediction
of cardiovascular events prediction of cognitive decline heart rate
fragmentation

Published: Feb. 14, 2024. Version: 1.0.0

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Challenge Credentialed Access


SNOMED CT ENTITY LINKING CHALLENGE

Will Hardman, Mark Banks, Rory Davidson, Donna Truran, Nindya Widita
Ayuningtyas, Hoa Ngo, Alistair Johnson, Tom Pollard

272 discharge notes from the MIMIC-IV-Note dataset annotated with SNOMED CT
concepts.

snomed entity linking clinical annotation

Published: Dec. 19, 2023. Version: 1.0.0

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Database Open Access


VITALDB, A HIGH-FIDELITY MULTI-PARAMETER VITAL SIGNS DATABASE IN SURGICAL
PATIENTS

Hyung-Chul Lee, Chul-Woo Jung

VitalDB, a high-fidelity multi-parameter vital signs database in surgical
patients

waveform anesthesia vitaldb intraoperative biosignal ecg

Published: Sept. 21, 2022. Version: 1.0.0

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Database Credentialed Access


MIMIC-IV

Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi,
Roger Mark

Large database of de-identified health information from patients admitted to
Beth Israel Deaconess Medical Center

mimic critical care machine learning intensive care unit

Published: Jan. 6, 2023. Version: 2.2

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Database Credentialed Access


MIMIC-CXR DATABASE

Alistair Johnson, Tom Pollard, Roger Mark, Seth Berkowitz, Steven Horng

Chest radiographs in DICOM format with associated free-text reports.

mimic computer vision machine learning chest x-rays radiology natural language
processing

Published: Sept. 19, 2019. Version: 2.0.0

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Database Credentialed Access


BRAX, A BRAZILIAN LABELED CHEST X-RAY DATASET

Eduardo Pontes Reis, Joselisa Paiva, Maria Carolina Bueno da Silva, Guilherme
Alberto Sousa Ribeiro, Victor Fornasiero Paiva, Lucas Bulgarelli, Henrique Lee,
Paulo Victor dos Santos, vanessa brito, Lucas Amaral, Gabriel Beraldo, Jorge
Nebhan Haidar Filho, Gustavo Teles, Gilberto Szarf, Tom Pollard, Alistair
Johnson, Leo Anthony Celi, Edson Amaro

BRAX contains 24,959 chest radiography exams and 40,967 images acquired in a
large general Brazilian hospital. All images have been read by trained
radiologists and 14 labels were derived from Brazilian Portuguese reports using
NLP.

chest x-ray dataset artificial intelligence

Published: June 17, 2022. Version: 1.1.0

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LATEST RESOURCES


Database Credentialed Access


RADIALOG INSTRUCT DATASET

Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Nassir Navab, Matthias Keicher

Image-based instruct data for Chest X-Ray understanding and analysis.

radiology report generation large vision-language models medical image
understaning radiology assistant radiology chatbot

Published: March 25, 2024. Version: 1.0.0

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Database Open Access


PADS - PARKINSONS DISEASE SMARTWATCH DATASET

Julian Varghese, Alexander Brenner, Lucas Plagwitz, Catharina van Alen, Michael
Fujarski, Tobias Warnecke

The PADS dataset contains smartwatch-based records from interactive neurological
assessments of Parkinsons disease patients, differential diagnoses and healthy
controls. The data is complemented with non-motor symptoms and medical history
information

wearables movement disorders parkinsons disease

Published: March 25, 2024. Version: 1.0.0

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Database Open Access


SCIENTISST MOVE: ANNOTATED WEARABLE MULTIMODAL BIOSIGNALS RECORDED DURING
EVERYDAY LIFE ACTIVITIES IN NATURALISTIC ENVIRONMENTS

João Areias Saraiva, Mariana Abreu, Ana Sofia Carmo, Hugo Plácido da Silva, Ana
Fred

Multimodal (ECG, EMG, EDA, PPG, TEMP, ACC) biosignal dataset of everyday
activities. Created with 3 wearable devices based on ScientISST Sense and
Empatica E4.

multimodal wearable greet lift uncontrolled environments run jump gesticulate
walk

Published: March 25, 2024. Version: 1.0.1

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Model Credentialed Access


ASCLEPIUS-R : CLINICAL LARGE LANGUAGE MODEL BUILT ON MIMIC-III DISCHARGE
SUMMARIES

Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae,
Jungwoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon
Han, Yoon Bin Jung, Yohan Jo, Edward Choi

Asclepius: Publicly Available Clinical Large Language Models with Synthetic
Clinical Notes Asclepius-R: A instruction-finetuned large language model with
MIMIC-III clinical notes

clinical notes large language model synthetic clinical notes synthetic notes
asclepius open-source llm clinical llm

Published: March 25, 2024. Version: 1.1.0

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Database Open Access


RESPIRATORY AND HEART RATE MONITORING DATASET FROM AERATION STUDY

Ella Frances Sophia Guy, Isaac Flett, Jaimey Anne Clifton, Trudy Caljé-van der
Klei, Rongqing Chen, Jennifer Knopp, Knut Moeller, James Geoffrey Chase

Respiratory and cardiovascular data collected from 20 subjects. Pressure, flow,
aeration, and heart-rate data were collected during trials which included
resting breathing, CPAP at varied PEEP settings, breath-holds, and forced
expiratory manoeuvres.



Published: March 20, 2024. Version: 1.0.0

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Database Restricted Access


CHEXCHONET: A CHEST RADIOGRAPH DATASET WITH GOLD STANDARD ECHOCARDIOGRAPHY
LABELS

Pierre Elias, Shreyas Bhave

Early detection of heart failure is vital for improving outcomes. The dataset
contains 71,589 CXRs paired with gold standard labels from echocardiograms to
enable the training of models to detect pathologies indicative of early stage
heart failure.

heart failure chest x-rays deep learning early detection cardiac structural
abnormalties

Published: March 20, 2024. Version: 1.0.0

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More resources


NEWS



GEORGE B. MOODY PHYSIONET CHALLENGE 2024: CHALLENGE UPDATE - PARTNERING WITH
DATA SCIENCE AFRICA AND

March 14, 2024

We are delighted to announce that the George B. Moody PhysioNet Challenges are
partnering with Data Science Africa (DSA) and the IEEE Signal Processing
Society's Challenges and Data Collections Committee (CDCC). The IEEE CDCC is
supporting this year’s Challenge with additional cash prizes for participating
teams from Africa and the Challenge organizers will be running a workshop at
this year's annual DSA meeting in Kenya. In connection with this, the Challenge
organizers will be running a workshop at DSA in Kenya from June 2-5th 2024.
Please note that we are also accepting (and scoring) entries, and there are two
deadlines coming up - April 8th to submit a preliminary entry to the
Challenge and April 15th to submit a (placeholder) abstract to CinC.

Read more: https://physionet.org/news/post/challenge-2024

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DUKE CRITICAL CARE DATATHON: 13-14 APRIL 2024

Feb. 7, 2024

Our colleagues at Duke are hosting a Critical Care Datathon on April 13-14,
2024. The Datathon is a collaborative two-day event that connects critical care
clinicians with data scientists to develop pragmatic data-driven models using
de-identified critical care electronic health record datasets.

Using de-identified critical care electronic health record datasets (including
MIMIC and the eICU Collaborative Research Database), participants will develop
new projects in 36 hours, from problem to abstract (and more)! 

Participants will be organized into teams that are half-data science,
half-clinical. You do not need to have a team; the organizers will help you find
a team. Questions will be crowdsourced. No experience is required.

 * If you are a clinician, your interest, but not expertise, in data science is
   required. 
 * If you are a data scientist, your interest, but not expertise, in healthcare
   and critical care is required. 

For more information, see: https://sites.duke.edu/datathon2024/

Read more: https://sites.duke.edu/datathon2024/

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CHIL 2024: SUBMIT YOUR PAPER BY FRIDAY, 16 FEBRUARY

Feb. 6, 2024

The 2024 Conference on Health, Inference, and Learning (CHIL) invites
submissions focused on artificial intelligence and machine learning (AI/ML)
techniques that address challenges in health, which includes clinical
healthcare, public health, health economics, informatics, and more. For full
details, refer to the online Call for
Papers: https://www.chilconference.org/call-for-papers.html 

This year, CHIL 2024 will accept submissions for three distinct tracks: Models
and Methods, Applications and Practice, and Policy, Impact and Society. Accepted
papers will be published in the Proceedings of Machine Learning Research (PMLR).
We are also offering Best Paper Awards to recognize outstanding work across all
tracks.

The deadline for submissions has been extended to: Friday, 16 Feb 2024 at
11:59pm AoE. Submit your paper
at: https://openreview.net/group?id=chilconference.org/CHIL/2024/Conference

 

Read more: https://www.chilconference.org/call-for-papers.html

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CONFERENCE ON HEALTH, INFERENCE, AND LEARNING (CHIL): SUBMIT YOUR PAPER BY MON 5
FEB, 2024!

Jan. 30, 2024

The 2024 Conference on Health, Inference, and Learning (CHIL) invites
submissions focused on artificial intelligence and machine learning (AI/ML)
techniques that address challenges in health, which includes clinical
healthcare, public health, health economics, informatics, and more. For full
details, refer to the online Call for
Papers: https://www.chilconference.org/call-for-papers.html 

This year, CHIL 2024 will accept submissions for three distinct tracks: Models
and Methods, Applications and Practice, and Policy, Impact and Society. Accepted
papers will be published in the Proceedings of Machine Learning Research (PMLR).
We are also offering Best Paper Awards to recognize outstanding work across all
tracks.

Submissions are due on February 5th, 11:59 PM EST in the form of anonymized PDF
files. All submissions for CHIL 2024 will be managed through
the OpenReview system. Similar to last year, we have a full author response
period and reviewer discussion period to ensure proper feedback on the work. 

Hosted by The Association of Health, Learning, and Inference (AHLI), the CHIL
conferences have consistently served as premier scientific meetings, uniting
clinicians and researchers from both industry and academia, and weaving a rich
tapestry of knowledge and innovation.

Building on a series of conferences and events since 2019, CHIL has persistently
set a benchmark in interdisciplinary research within the realms of machine
learning and health, demonstrated through its impactful sessions
(2020, 2021, 2022, 2023). Following the resounding success of CHIL 2023 at the
Broad Institute, Cambridge, we are thrilled to announce that CHIL 2024 will
continue fostering insightful discussions and collaborations in the field. The
5th annual conference will take place in-person from June 27-28 at the Verizon
Executive Education Center at Cornell Tech in New York City. 

Important Dates

 * Submissions due: Feb 5, 2024 at 11:59pm
 * Bidding opens for reviewers: Feb 6, 2024 at 11:59pm
 * Reviews released: Mar 4, 2024 by 11:59pm
 * Author/Reviewer discussion period: Mar 10-21, 2024
 * Author notification: Apr 3, 2024 by 11:59pm
 * CHIL conference: June 27-28, 2024

Read more: https://chilconference.org/

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GEORGE B. MOODY PHYSIONET CHALLENGE 2024: CHALLENGE OPENING

Jan. 26, 2024

We are delighted to announce the opening of the George B. Moody PhysioNet
Challenge 2024. The 2024 Challenge invites teams to develop algorithms for
digitizing and classifying electrocardiograms (ECGs) captured from images or
paper printouts.

Despite recent advances in digital ECG devices, paper or physical ECGs remain
common, especially in the Global South. These paper ECGs document the history
and diversity of cardiovascular diseases (CVDs), and algorithms that can
digitize and classify these images have the potential to improve our
understanding and treatment of CVDs, especially for underrepresented and
underserved populations.

We have shared example code and scoring code in both MATLAB and Python and
synthetic ECG generation code in Python. While last year’s Challenge had the
largest dataset yet, this year’s Challenge begins with a much more tractable
dataset that you may already have on your machine, and you can use the provided
code to create ECG images with realistic artifacts. We will also augment these
data to create a much richer and more representative dataset, so stay tuned for
more announcements. We will open the scoring system in the coming days.

See the Challenge website for more information, rules and
deadlines: https://physionetchallenges.org/2024/

As in previous years, we have divided the Challenge into two phases: an
unofficial phase and an official phase. The unofficial phase solicits feedback
from the research community (i.e., you) to help us to improve the Challenge for
the official phase, so we require teams to register and participate in the
unofficial phase of the Challenge to be eligible for a prize. Please enter early
and often – we need you to look for quirks in our data, our scoring system, and
otherwise. We are imperfect (and bandwidth-limited), so please send us
suggestions via the forum (see below). We rely on the community to help us to
improve the quality of the Challenge each year.

More information will be posted on the PhysioNet Challenge website and
the Challenge forum as it becomes available. Please post questions and comments
to the Challenge forum as well. However, if your question reveals information
about your entry, then please email info [at] physionetchallenge.org instead to
help us safeguard the diversity of approaches to the Challenge. We may post
parts of our replies publicly if we feel that all Challengers should benefit
from the information contained in our responses. We will not answer emails about
the Challenge sent to other email addresses.

Many thanks again for your continued support of this event, and we hope that you
enjoy the 2024 Challenge!

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More news

PhysioNet is a repository of freely-available medical research data, managed by
the MIT Laboratory for Computational Physiology.

Supported by the National Institute of Biomedical Imaging and Bioengineering
(NIBIB) under NIH grant number R01EB030362.

For more accessibility options, see the MIT Accessibility Page.

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