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Featured Resources

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

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 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

critical care intensive care unit machine learning mimic

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.

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

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 dataset artificial intelligence

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

MedDec: Medical Decisions for Discharge Summaries in the MIMIC-III Database

Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, Hadi Amiri, Leo Anthony Celi

Annotations of ten types of medical decisions from discharge summaries in the MIMIC-III database.

natural language processing medical decisions span classification discharge summary mimic

Published: Oct. 16, 2024. Version: 1.0.0

Database Contributor Review

A multimodal dental dataset facilitating machine learning research and clinic services

Wenjing Liu, Yunyou Huang, Suqin Tang

A new dental dataset that contains 169 patients, three commonly used dental image models, and images of various health conditions of the oral cavity.

Published: Oct. 11, 2024. Version: 1.1.0

Database Credentialed Access

MIMIC-IV

Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Brian Gow, Benjamin Moody, Steven Horng, Leo Anthony Celi, Roger Mark

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

critical care intensive care unit machine learning mimic

Published: Oct. 11, 2024. Version: 3.1

Database Credentialed Access

MIMIC-IV-Ext-BHC: Labeled Clinical Notes Dataset for Hospital Course Summarization

Asad Aali, Dave Van Veen, Yamin Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash Tehrani, Jangwon Kim, Akshay Chaudhari

This dataset presents a collection of preprocessed and labeled clinical notes derived from "MIMIC-IV-Note", and aims to facilitate the development of ML models focused on summarizing brief hospital courses (BHC) from clinical notes.

natural language processing clinical notes brief hospital course text summarization machine learning

Published: Oct. 10, 2024. Version: 1.1.0

Database Open Access

VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors

Li-wei Lehman, Benjamin Moody, Lucas McCullum, Hasan Saeed, Harsh Deep, Diane Perry, Tristan Struja, Qiao Li, Gari Clifford, Roger Mark

VTaC is an annotated ventricular tachycardia (VT) arrhythmia alarm database containing over 5,000 waveform recordings with VT alarms from ICU monitors, with each alarm labeled as either true or false by at least two human expert annotators.

arrhythmia icu false alarms benchmark dataset ventricular tachycardia machine learning

Published: Oct. 1, 2024. Version: 1.0

Visualize waveforms

Database Credentialed Access

Comprehensive Polysomnography (CPS) Dataset: A Resource for Sleep-Related Arousal Research

Stefan Kraft, Andreas Theissler, Vera Wienhausen-Wilke, Philipp Walter, Gjergji Kasneci

This dataset includes polysomnographic sleep recordings from a study on sleep-related arousal diagnostics, featuring raw and derived data channels, annotated event types, and questionnaire data.

polysomnography sleep disorders machine learning in healthcare sleep arousal diagnostics pulse wave analysis

Published: Sept. 18, 2024. Version: 1.0.0

More resources

News

Upgrading MIMIC-IV on BigQuery

News from: MIMIC-IV v3.0.

Sept. 28, 2024

MIMIC-IV v3.0 is currently unavailable on BigQuery. Currently, the mimiciv_hosp and mimiciv_icu datasets on BigQuery contain MIMIC-IV v2.2, and access to these datasets is still available via PhysioNet by navigating to the v2.2 page. We plan to upgrade these BigQuery datasets in the near future, and will notify users when we do so.

Delays in reviewing applications for credentialed access

June 17, 2024

As a result of staffing changes in our research group, applications for credentialed access are likely to be subject to significant delays. We are doing our best to deal with the waitlist quickly, handling applications in the order in which they are received. To help ensure that your application is successful, please remember to:

Include a copy of your CITI training report (not the certificate).
Remind your reference to reply promptly when contacted.
Check your application details are correct before submitting.
Add an institutional or educational email address as your primary email.

We apologize for the inconvenience. Please bear with us during this busy time!

George B. Moody PhysioNet Challenge 2024 is teaming up with Data Science Africa

May 18, 2024

The George B. Moody PhysioNet Challenge 2024 is teaming up with Data Science Africa (DSA) to innovate cardiac care through signal processing & machine learning at Nyeri, Kenya, for DSA's 10 year anniversary in June 2024.

DSA aims to create a hub in the network of data science researchers across Africa and strengthen the African data science community. #DSA2024Nyeri https://www.datascienceafrica.org/dsa2024nyeri/blog/summer-school

Guidelines for creating datasets and models from MIMIC

News from: MIMIC-IV v2.2.

April 24, 2024

We recognize that there is value in creating datasets or models that are either derived from MIMIC or which augment MIMIC in some way (for example, by adding annotations). Here are some guidelines on creating these datasets and models:

Any derived datasets or models should be treated as containing sensitive information. If you wish to share these resources, they should be shared on PhysioNet under the same agreement as the source data.
If you would like to use the MIMIC acronym in your project name, please include the letters “Ext” (for example, MIMIC-IV-Ext-YOUR-DATASET"). Ext may either indicate “extracted” (e.g. a derived subset) or “extended” (e.g. annotations), depending on your use case.

Network issues at MIT, impacting the availability of PhysioNet

April 9, 2024

We are currently experiencing network issues at MIT, impacting the availability of PhysioNet services. We apologize for any inconvenience this may cause and are working to resolve the issue as soon as we can.

More news