Predicting Clinical Events via Graph Neural Networks

Timely detection of clinical events would provide healthcare providers the opportunity to make meaningful interventions that can result in improved health outcomes. This work describes a methodology developed at a large U.S. healthcare insurance company for predicting clinical events using administrative claims data. Most of the existing literature for predicting clinical events leverage historical data in Electronic Health Records (EHR). EHR data however has limitations making it undesirable for real-time use-cases. It is inconsistent, expensive, inefficient and sparsely available. In contrast, administrative claims data is relatively consistent, efficient and readily available. In this work, we introduce a novel modeling workflow: First, we learn custom embeddings for medical codes within claims data in order to uncover the hidden relationships between them. Second, we introduce a novel way of representing a member’s health history with a graph such that the relationships between various diagnosis and procedure codes is captured. Finally, we apply Graph Neural Networks (GNN) to perform a multi-label graph classification for clinical event prediction. Our approach produces more accurate predictions than any other standard classification approaches and can be easily generalized to other clinical prediction tasks.