The electronic health record (EHR) is an essential data resource that improves medical decision-making and health service delivery monitoring and allows for developing predictive models for early risk scoring, among other applications. EHR-based predictive models have improved with the use of deep learning (DL) techniques, which excel when there are large amounts of data and potentially complex relationships between input features and the target prediction. However, EHR data possess unique characteristics such as complicated dependency structures between events, event frequency, and missing patient subpopulation data, to name a few issues (Lee et al., 2024). These dimensions of EHR data have led to the use of DL methods that are not typically used in standard image, speech, and natural language processing but instead are specifically designed to address the demands of EHR data analysis, for details: Read More.
