Resources

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

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

The goal of the challenge is use information from the available signals to correctly classify target arousal regions.

A challenge to explore real clinical questions in critically ill patients using the MIMIC. A collaboration with the IEEE Conference on Biomedical and Health Informatics 2018 and the IEEE Conference on Body Sensor Networks.

The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an al…

The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single sho…

The 2015 PhysioNet/CinC Challenge aims to encourage the development of algorithms to reduce the incidence of false alarms in the Intensive Care Unit (ICU).

This is the dataset for SemEval 2014 and 2015, Analysis of Clinical Text

This challenge aims to encourage the exploration of robust methods for locating heart beats in continuous long-term data from bedside monitors and similar devices that record not only ECG but usually other physiologic signals as well, including puls…

The ShARe/CLEF eHealth 2014 Challenge (Task 2) on Disorder Attributes in Clinical Reports