Resources


Challenge Open Access

Predicting Paroxysmal Atrial Fibrillation/Flutter: The PhysioNet/Computing in Cardiology Challenge 2001

Following the success of the first Computers in Cardiology Challenge, we are pleased to offer a new challenge from PhysioNet and Computers in Cardiology 2001. The challenge is to develop a fully automated method to predict the onset of paroxysmal at…

challenge atrial fibrillation ecg

Published: March 1, 2001. 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, Supreeth Shashikumar, Benjamin Moody, M. Brandon Westover, Ashish Sharma, Shamim Nemati, Gari D. Clifford

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 Open Access

A Wearable Exam Stress Dataset for Predicting Cognitive Performance in Real-World Settings

Md Rafiul Amin, Dilranjan Wickramasuriya, Rose T Faghih

The data contains electrodermal activity, heart rate, blood volume pulse, skin surface temperature, inter beat interval and accelerometer data recorded during three exam sessions (midterm 1, midterm 2 and finals) as well as their corresponding grades

stress

Published: May 26, 2022. 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, Supreeth Shashikumar, Benjamin Moody, M. Brandon Westover, Ashish Sharma, Shamim Nemati, Gari D. Clifford

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

Nosocomial Risk Datasets from MIMIC-III

Travis Goodwin

Text-based Longitudinal Data for Predicting Nosocomial Disease Risk as used by CANTRIP.

pressure injury risk prediction acute kidney injury anemia forecasting natural language processing deep learning

Published: Sept. 15, 2022. Version: 1.0

Database Credentialed Access

Nosocomial Risk Datasets from MIMIC-III

Travis Goodwin

Text-based Longitudinal Data for Predicting Nosocomial Disease Risk as used by CANTRIP.

pressure injury risk prediction acute kidney injury anemia forecasting natural language processing deep learning

Published: Sept. 15, 2022. Version: 1.0

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, Omar Badawi

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

In-hospital physical activity measured with a new Bosch accelerometer sensor system

Severin Schricker, Nico Schmid, Moritz Schanz, Martin Kimmel, Mark Dominik Alscher

Measurements of physical activity with wrist-worn Bosch sensor platform to test predictive performance for the duration of hospitalization and readmission in 58 patients with acute illnesses in internal medicine

prediction acute illness hospitalization readmission accelerometry accelerometer

Published: Dec. 3, 2020. Version: 1.0

Database Open Access

Induced Cesarean EHG DataSet (ICEHG DS): An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

Franc Jager

The design and development of ICEHG DS was funded by the Slovenian Research Agency (ARRS) under the research project Metabolic and inborn factors of reproductive health, birth III.

neuroelectric pregnancy electrohysterogram cesarean-section delivery induced delivery

Published: Oct. 8, 2023. Version: 1.0.1

Visualize waveforms

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, Omar Badawi

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