PhysioNet

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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

Database Open Access

PTB-XL, a large publicly available electrocardiography dataset

Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Wojciech Samek, Tobias Schaeffter

The PTB-XL ECG dataset is a large dataset of 21801 clinical 12-lead ECGs from 18869 patients of 10 second length. The raw signal data has been annotated by up to two cardiologists with 71 different ECG statements and is supplemented by rich metadata.

ptb-xl ptb electrocardiography ecg

Published: Nov. 9, 2022. Version: 1.0.3

Visualize waveforms

Database Credentialed Access

MIMIC-IV

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

Database of patients admitted to the Beth Israel Deaconess Medical Center

mimic machine learning critical care intensive care unit

Published: Jan. 6, 2023. Version: 2.2

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

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 artificial intelligence dataset

Published: June 17, 2022. Version: 1.1.0

Database Credentialed Access

eICU Collaborative Research Database

Tom Pollard, Alistair Johnson, Jesse Raffa, Leo Anthony Celi, Omar Badawi, Roger Mark

Multi-center database comprising deidentified health data associated with over 200,000 admissions to ICUs across the United States between 2014-2015.

telemedicine icu critical care

Published: April 15, 2019. Version: 2.0

Latest Resources

Database Credentialed Access

MS-CXR-T: Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Pérez-García, Max Ilse, Daniel Coelho de Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Hannah Richardson, Tristan Naumann, Javier Alvarez Valle, Ozan Oktay

The MS-CXR-T is a multimodal benchmark that enhances the MIMIC-CXR v2 dataset by including expert-verified annotations. Its goal is to evaluate biomedical visual-language processing models in terms of temporal semantics extracted from image and text.

multimodal radiology chest x-ray cxr disease progression vision-language processing

Published: March 17, 2023. Version: 1.0.0

Database Credentialed Access

GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

Xuhai Xu, Han Zhang, Yasaman Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin Kuehn, Mike Merrill, Paula Nurius, Shwetak Patel, Tim Althoff, Margaret Morris, Eve Riskin, Jennifer Mankoff, Anind Dey

GLOBEM datasets contain the first released multi-year mobile and wearable sensing datasets from 2018 to 2021, containing 705 person-years and 497 unique participants.

health ubiquitous computing well-being passive mobile sensing human behavior modeling

Published: March 14, 2023. Version: 1.1

Database Credentialed Access

SCRIPT CarpeDiem Dataset: demographics, outcomes, and per-day clinical parameters for critically ill patients with suspected pneumonia

Nikolay Markov, Catherine A Gao, Thomas Stoeger, Anna Pawlowski, Mengjia Kang, Prasanth Nannapaneni, Rogan Grant, Luke Rasmussen, Daniel Schneider, Justin Starren, Richard Wunderink, GR Scott Budinger, Alexander Misharin, Benjamin Singer, NU SCRIPT Study Investigators

SCRIPT seeks to delineate the host/pathogen interactions during pneumonia using multiomic analysis of bronchoalveolar lavage fluid joined with clinical data and physician adjudication.

Published: March 13, 2023. Version: 1.1.0

Database Credentialed Access

A Brazilian Multilabel Ophthalmological Dataset (BRSET)

Luis Filipe Nakayama, Mariana Goncalves, Lucas Zago Ribeiro, Helen Santos, Daniel Ferraz, Fernando Malerbi, Leo Anthony Celi, Caio Regatieri

This is the first Brazilian Multilabel Ophthalmological Dataset with demographic information and retinal photos labeled images according to anatomical parameters, quality control, and presumed diagnosis.

dataset ophthalmology retina

Published: March 8, 2023. Version: 1.0.0

Database Open Access

BIG IDEAs Lab Glycemic Variability and Wearable Device Data

Peter Cho, Juseong Kim, Brinnae Bent, Jessilyn Dunn

Glucose measurements and wrist-worn wearable sensor data from highnormoglycemic participants.

biomedical engineering biomarkers pre-diabetes

Published: March 6, 2023. Version: 1.1.0

Database Credentialed Access

MIMICEL: MIMIC-IV Event Log for Emergency Department

Jia Wei, Zhipeng He, Chun Ouyang, Catarina Moreira

MIMIC-IV Event Log for Emergency Department

Published: March 6, 2023. Version: 2.0.0

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News

Toronto Health Datathon (23-24 February 2023)

March 10, 2023

Over 45 students, academics, clinicians, and engineers gathered at the Google Canada offices on 23-24 February for the Toronto Health Datathon 2023. Participants used anonymized real-world data from Health Data Nexus to develop machine learning models aimed at solving real-world problems facing Canadian healthcare.

Over the past two years, PhysioNet has been collaborating with Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM) at the University of Toronto to develop the software that underpins both Health Data Nexus and PhysioNet. We look forward to continuing this collaboration, working towards tight integration between the two platforms.

Tokyo Datathon on Machine Learning in Healthcare (1-3 Sept 2023)

March 9, 2023

We are excited to be supporting the 3rd Tokyo Datathon on Machine Learning in Healthcare, co-organized by Tokyo Medical and Dental University and MIT Critical Data. The event will be held on 1-3 September 2023 and will bring together experts from across healthcare and data science to tackle clinical questions.

Registration and event details will be posted on the Datathon Website. If you are a Japanese-language speaker with experience with the MIMIC dataset and would be interested in helping as a mentor at the event, please reach out to Leo Anthony Celi.

PhysioNet 2023 Challenge Opening

Feb. 22, 2023

We are delighted to announce the opening of the George B. Moody PhysioNet Challenge 2023. This year’s Challenge invites teams to use electroencephalogram (EEG) recordings to predict the neurological recovery of patients from coma in the hours following resuscitation from cardiac arrest. This Challenge leverages a novel database of over 1,000 subjects from seven hospitals who together underwent over 50,000 hours of EEG monitoring. As always, the team with the best score for this task on the hidden test set wins the Challenge.

We have shared data, example code, and scoring code in both MATLAB and Python, and we will open the scoring system in the coming weeks. 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 and share the quirks in our data, our scoring system, and otherwise.

Please see the Challenge website and the Challenge forum for more information, rules and deadlines: https://physionetchallenges.org/2023/
https://groups.google.com/g/physionet-challenges/

Many thanks again for your continued support of this event, and we hope that you enjoy the 2023 Challenge.

Dataworks! Prizes Awarded to PhysioNet Challenge and MIT Critical Data teams

Feb. 22, 2023

We are delighted to announce that the George B. Moody PhysioNet Challenges were awarded the "Distinguished Achievement Award for Data Reuse, as part of the DataWorks! Prize, while MIT Critical Data was awarded "Significant Achievement Award for Data Sharing".

Launched on May 11, 2022, the Data Works! Prize was created in partnership between the NIH Office of Data Science Strategy and the Federation of American Societies for Experimental Biology (FASEB) to highlight the critical role of data sharing and reuse in scientific discovery.

George B. Moody designed and led the Challenges from 2000 to 2015. Prof. Clifford has led the Challenges since 2015 and has been a key contributor to its parent resource, PhysioNet (The Research Resource for Complex Physiologic Signals), for over two decades. Prof. Reyna has co-led the PhysioNet Challenges since 2019, and has been instrumental in the development of its repeatable science standards.

MIT Critical Data, led by the Laboratory for Computational Physiology, builds communities around the world across disciplines to derive knowledge from data routinely collected in the process of care in order to understand health and disease better, and in the local context. Its flagship project is the Medical Information Mart for Intensive Care, or the MIMIC database.

More on the DataWorks! Prize here: https://datascience.nih.gov/director/directors-blog-dataworks-winners-2023 and the PhysioNet Challenges here: https://physionetchallenges.org/faq and MIT Critical Data here: https://criticaldata.mit.edu/.

PhysioNet and MIMIC are supported by the National Institute of Biomedical Imaging and Bioengineering.

Opportunity to join the UCSF Hypoxia Lab as Data Analyst

Feb. 8, 2023

Our colleagues at UCSF Department of Anesthesia, The UCSF Hypoxia Lab (hypoxialab.org) and the UCSF Center for Health Equity in Surgery and Anesthesia (chesa.ucsf.edu) are seeking a full-time Data Analyst (with data engineering skills) to join their Open Oximetry project.

This project seeks to understand the potential impact of skin color on accuracy of pulse oximetry and other medical diagnostic devices. The Data Analyst will work at the intersection of health diagnostics, health equity and AI in the world’s leading lab (hypoxialab.org) for this type of research along with a team of experts who have published some of the seminal research on this topic.

As part of the project, the team will be setting up robust data collection systems in the lab and the hospital settings as well as creating and managing an open access data repository for diagnostics device data. This repository will contain data from our lab as well as data from collaborating study groups. The data will be shared via portals that facilitate raw data utilization for researchers and industry, as well as visualized data to help lay persons and consumers better understand device technology performance and standards.

The Data Analyst will work closely with the laboratory-based clinical research team to gather, analyze, and interpret a wide variety of research data; Design and conduct research including selecting data, developing research instruments, analyzing collected information according to established statistical methods, and developing recommendations based on research findings; Prepare reports, charts, tables, and other visual aids to interpret and communicate data and results; Create and manage data repositories; work with our AI/ML team to test novel analytic methods with our data. The ideal candidate will be knowledgeable in SQL and Python or R and have a Bachelors or Masters degree in Statistics, Data Science, or adjacent technical field.

This position is for a period of 12 months, though may be longer contingent on further funding. For further details, see the job posting.

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