RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

1 code implementation • NeurIPS 2016 • Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, Jimeng Sun

RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.

Disease Trajectory Forecasting

Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction

1 code implementation • 11 Feb 2016 • Edward Choi, Andy Schuetz, Walter F. Stewart, Jimeng Sun

Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data.

Representation Learning

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

1 code implementation • 18 Nov 2015 • Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Jimeng Sun

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.