Deep Learning with Heterogeneous Graph Embeddings for Mortality\n Prediction from Electronic Health Records

Abstract

Computational prediction of in-hospital mortality in the setting of an\nintensive care unit can help clinical practitioners to guide care and make\nearly decisions for interventions. As clinical data are complex and varied in\ntheir structure and components, continued innovation of modeling strategies is\nrequired to identify architectures that can best model outcomes. In this work,\nwe train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and\nuse the resulting embedding vector as additional information added to a\nConvolutional Neural Network (CNN) model for predicting in-hospital mortality.\nWe show that the additional information provided by including time as a vector\nin the embedding captures the relationships between medical concepts, lab\ntests, and diagnoses, which enhances predictive performance. We find that\nadding HGM to a CNN model increases the mortality prediction accuracy up to\n4\\%. This framework serves as a foundation for future experiments involving\ndifferent EHR data types on important healthcare prediction tasks.\n

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