UCLA

Timely prediction of clinical adverse events is a ubiquitous and important problem. We present here a timely risk scoring algorithm (Deep Risk) based on a novel Deep Learning architecture that solves the following key challenges: 1) the statistical properties of the physiological time-series data streams are not constant over time; 2) timely prediction is of the essence; 3) different patients exhibit different physiological trajectories; 4) the data is unbalanced (adverse events are uncommon). Deep Risk employs a Gated Recurrent Unit (GRU)-based Recurrent Neural Network (RNN) to aggregate the predictions of a family of GRU-based RNN's which operate on time windows of varying lengths. We show that shorter windows cope better with the non-stationary data but longer windows are capable of issuing more timely predictions. Using both shorter and longer windows enables Deep Risk to do both. Each "lower level" RNN uses the information in its time window to make a prediction; the "higher level" RNN uses the information in the longest of these time intervals to aggregate the predictions of the "lower level" RNN's and issue a final prediction. We perform simulations based on real-world medical data sets and show that Deep Risk achieves large and significant performance improvement over other methods, including clinical risk scores and state-of-the-art machine learning algorithms.