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

TL;DR

This paper introduces a novel approach combining heterogeneous graph embeddings with CNNs to improve in-hospital mortality prediction from complex electronic health records, demonstrating up to 4% accuracy improvement.

Contribution

It presents a new framework integrating heterogeneous graph models with CNNs for EHR data, capturing temporal relationships to enhance mortality prediction accuracy.

Findings

Adding HGM increases prediction accuracy by up to 4%.

Embedding time as a vector improves modeling of medical concept relationships.

Framework provides a basis for future healthcare prediction experiments.

Abstract

Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modeling strategies is required to identify architectures that can best model outcomes. In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captures the relationships between medical concepts, lab tests, and diagnoses, which enhances predictive performance. We find that adding HGM to a CNN model increases the mortality prediction accuracy up to 4\%. This framework serves as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.