Resources


Challenge Restricted Access

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction

Meredith Lee, Jesse Raffa, Marzyeh Ghassemi, et al.

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction focuses on patient health. Join a team, explore the data, and share your insights: http://bit.ly/WiDSdatathon2020

mortality risk data science kaggle icu challenge predictive analytics women in data science

Published: Jan. 22, 2020. Version: 1.0.0

Challenge Open Access

Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012

The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. Participants will use information collected during the first two days of an ICU stay to predict which patients survive the…

mortality prediction challenge ehr mimic

Published: Jan. 20, 2012. Version: 1.0.0

Challenge Restricted Access

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction

Meredith Lee, Jesse Raffa, Marzyeh Ghassemi, et al.

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction focuses on patient health. Join a team, explore the data, and share your insights: http://bit.ly/WiDSdatathon2020

mortality risk data science kaggle icu challenge predictive analytics women in data science

Published: Jan. 22, 2020. Version: 1.0.0

Challenge Open Access

Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012

The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. Participants will use information collected during the first two days of an ICU stay to predict which patients survive the…

mortality prediction challenge ehr mimic

Published: Jan. 20, 2012. Version: 1.0.0

Challenge Open Access

Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019

Matthew Reyna, Chris Josef, Russell Jeter, et al.

The 2019 PhysioNet Computing in Cardiology Challenge invites participants to predict sepsis in clinical data

prediction challenge sepsis

Published: Aug. 5, 2019. Version: 1.0.0

Database Credentialed Access

GOSSIS-1-eICU, the eICU-CRD subset of the Global Open Source Severity of Illness Score (GOSSIS-1) dataset

Jesse Raffa, Alistair Johnson, Tom Pollard, et al.

GOSSIS-1 is an in-hospital mortality prediction algorithm for critical care patients. GOSSIS-1 was trained using data from three countries. This dataset corresponds with the USA subset of the GOSSIS-1 dataset for the 2022 publication below.

icu critical care severity of illness global gossis apache mortality prediction benchmarking

Published: July 20, 2022. Version: 1.0.0

Database Restricted Access

Community-Acquired Pneumonia, Endotypes and Phenotypes (NACef): Prospective, observational cohort study of Translational Medicine

Natalia Sanabria-Herrera, Esteban Garcia Gallo, Luis Felipe Reyes

Community-Acquired Pneumonia (CAP) poses a significant health risk, linked to high in-hospital morbidity and mortality rates. The dataset includes clinical details of 768 CAP patients at Clinica Universidad de La Sabana, Colombia.

Published: Aug. 21, 2025. Version: 2.0.1

Database Credentialed Access

Immunosuppressive Condition and Medication Annotations for Admission Notes in the MIMIC-III Database

Vijeeth Guggilla, Melissa Bak, Mengjia Kang, et al.

This database contains 200 MIMIC-III admission notes with adjudicated labels for histories of various immunosuppressive conditions and usage of various immunosuppressive medications.

Published: Aug. 4, 2025. Version: 1.0.0

Database Open Access

MUSIC (Sudden Cardiac Death in Chronic Heart Failure)

Alba Martin-Yebra, Juan Pablo Martínez, Pablo Laguna

The MUSIC study is a prospective, multicentre, longitudinal study designed to assess risk predictors of cardiac mortality and sudden cardiac death in ambulatory patients with chronic heart failure.

Published: Jan. 24, 2025. Version: 1.0.1

Visualize waveforms

Database Credentialed Access

MIMIC-IV-Ext-MDS-ED: Multimodal Decision Support in the Emergency Department - a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine

Juan Miguel Lopez Alcaraz, Nils Strodthoff

MIMIC-IV-ext-MDS-ED proposes a dataset to benchmark multimodal decision support in the emergency department. It features multimodal input (including ECG waveforms) and a comprehensive set of prediction targets (diagnoses and deterioration prediction)

emergency department ecg diagnoses prediction deterioration prediction benchmark multimodal

Published: Sept. 12, 2024. Version: 1.0.0