Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records
We present an interpretable neural network for predicting an important clinical outcome (1-year mortality) from multi-modal Electronic Health Record (EHR) data. Our approach builds on prior multi-modal machine learning models by now enabling visualization of how individual factors contribute to the overall outcome risk, assuming other factors remain constant, which was previously impossible. 
We demonstrate the value of this approach using a large multi-modal clinical dataset including both EHR data and 31,278 echocardiographic videos of the heart from 26,793 patients. We generated separate models for (i) clinical data only (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numeric variables derived from the videos, which we call echocardiography-derived measures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretable multi-modal model maintained performance compared to non-interpretable models (Random Forest, XGBoost), and also performed significantly better than a model using a single modality (average AUC=0.82). Clinically relevant insights and multi-modal variable importance rankings were also facilitated by the new model, which have previously been impossible.