Multi-Level Embedding with Topic Modeling on Electronic Health Records for Predicting Depression

Abstract

Deep learning methods have exhibited impressive performance in many classification and prediction tasks in healthcare using electronic health record (EHR) data. However, effectively handling the heterogeneous nature and sparsity of EHR data remain as challenges. In this work, we present a model that utilizes heterogeneous data and attempts to address sparsity using temporal Multi-Level Embeddings of diagnoses, procedures, and medication codes with demographic information and Topic modeling (MLET). MLET aggregates various categories of EHR data and learns inherent structure based on hospital visits in a patient’s medical history. We demonstrate the potential of the approach in the task of predicting depression using different time windows prior to a clinical diagnosis. We found that MLET outperformed all baseline methods with a highest improvement from 0.6122 to 0.6808 in precision recall area under the curve (PRAUC). Our results demonstrate the model’s ability to utilize heterogeneous EHR information to predict depression, which may have future implications for screening and early detection.