Deep Interpretable Mortality Model for Intensive Care Unit Risk Prediction

Abstract

Estimating the mortality of patients plays a fundamental role in an intensive care unit (ICU). Currently, most learning approaches are based on deep learning models. However, these approaches in mortality prediction suffer from two problems: (i) the specificity of causes of death are not considered in the learning process due to the different diseases, and symptoms are mixed-used without diversification and localization; (ii) the learning outcome for the mortality prediction is not self-explainable for the clinicians. In this paper, we propose a Deep Interpretable Mortality Model (DIMM), which employs Multi-Source Embedding, Gated Recurrent Units (GRU), Attention mechanism and Focal Loss techniques to prognosticate mortality prediction. We intensified the mortality prediction by considering the different clinical measures, medical treatments and the heterogeneity of the disease. More importantly, for the first time, in this framework, we use a separate evidence-based interpreter named Highlighter to interpret the prediction model, which makes the prediction understandable and trustworthy to clinicians. We demonstrate that our approach achieves state-of-the-art performance in mortality prediction and can get an interpretable prediction on four different diseases.