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

Accuracy and interpretability are two dominant features of successful\npredictive models. Typically, a choice must be made in favor of complex black\nbox models such as recurrent neural networks (RNN) for accuracy versus less\naccurate but more interpretable traditional models such as logistic regression.\nThis tradeoff poses challenges in medicine where both accuracy and\ninterpretability are important. We addressed this challenge by developing the\nREverse Time AttentIoN model (RETAIN) for application to Electronic Health\nRecords (EHR) data. RETAIN achieves high accuracy while remaining clinically\ninterpretable and is based on a two-level neural attention model that detects\ninfluential past visits and…