Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions

Electronic Health Record (EHR) data is a rich source for powerful biomedical discovery but it consists of a wide variety of data types that are traditionally difficult to model. Furthermore, many machine learning frameworks that utilize these data for predictive tasks do not fully leverage the inter-connectivity structure and therefore may not be fully optimized. In this work, we propose a relational, deep heterogeneous network learning method that operates on EHR data and addresses these limitations. In this model, we used three different node types: patient, lab, and diagnosis. We show that relational graph learning naturally encodes structured relationships in the EHR and outperforms traditional feed forward models in the prediction of thousands of diseases. We evaluated our model on the EHR data derived from MIMIC-III, a public critical care data set, and show that our model has improved prediction of numerous diagnosis.