Graph-Based Data Representation and Prediction in Medical Domain Tasks Using Graph Neural Networks

Medical data often presents as a time series, reflecting the disease's progression. This can be captured through longitudinal health records or hospital treatment notes, encompassing diagnoses, health states, medications, and procedures. Understanding disease evolution is critical for effective treatment. Graph embedding of such data is advantageous, as it inherently captures entity relationships, offering significant utility in medicine. Hence, this study aims to develop a graph representation of Electronic Health Records (EHRs) and combine it with a method for predictive analysis of COVID-19 using network-based embedding. Evaluation of Graph Neural Networks (GNNs) against Recurrent Neural Networks (RNNs) reveals superior performance of GNNs, underscoring their potential in medical data analysis and forecasting.