The challenge is to create a model that uses data from the first 24 hours of intensive care to predict patient survival. MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.
Labeled training data are provided for model development; you will then upload your predictions for unlabeled data to Kaggle and these predictions will be used to determine the public leaderboard rankings, as well as the final winners of the competition.
The competition at widsconference.org/datathon and on the Kaggle Discussion Forum.
https://www.kaggle.com/c/widsdatathon2020/data
0.89404 (top - 0.91497)