Resources


Challenge Restricted Access

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

Meredith Lee, Jesse Raffa, Marzyeh Ghassemi, Tom Pollard, Sharada Kalanidhi, Omar Badawi, Karen Matthys, Leo Anthony Celi

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 ehr challenge mimic

Published: Jan. 20, 2012. 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 ehr challenge mimic

Published: Jan. 20, 2012. 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 ehr challenge mimic

Published: Jan. 20, 2012. 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, 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


Model Credentialed Access

What's in a Note? Unpacking Predictive Value in Clinical Note Representations

Tristan Naumann, William Boag

Word vectors corresponding to the AMIA 2018 Informatics Summit paper of the same name.

Published: Jan. 7, 2018. Version: 0.1


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


Challenge Open Access

Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020

Erick Andres Perez Alday, Annie Gu, Amit Shah, Chengyu Liu, Ashish Sharma, Salman Seyedi, Ali Bahrami Rad, Matthew Reyna, Gari Clifford

The goal of the 2020 PhysioNet - Computing in Cardiology Challenge is to design and implement a working, open-source algorithm that can automatically identify cardiac abnormalities in 12-lead ECG recordings.

Published: July 29, 2022. Version: 1.0.2

Visualize waveforms

Database Credentialed Access

MIMIC-III and eICU-CRD: Feature Representation by FIDDLE Preprocessing

Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael Sjoding, Jenna Wiens

Features and labels from MIMIC-III and eICU-CRD produced by FIDDLE, an EHR preprocessing pipeline.

preprocessing machine learning electronic health record

Published: April 28, 2021. Version: 1.0.0


Database Credentialed Access

MIMIC-III - SequenceExamples for TensorFlow modeling

Jonas Kemp, Kun Zhang, Andrew Dai

MIMIC-III data converted into TensorFlow SequenceExample format, for use in modeling pipelines.

tensorflow sequence modeling machine learning deep learning

Published: Sept. 29, 2020. Version: 1.0.0