Longitudinal EHR Modeling

Longitudinal electronic health record modeling studies how diagnoses, measurements, treatments, notes, and other events change across a patient’s care. It supports tasks such as early risk prediction, disease trajectory forecasting, medication recommendation, and treatment-effect estimation. Common approaches include temporal transformers and recurrent networks, continuous-time models, graph and state-space methods, and multimodal or language-model-based representations. A central challenge is making predictions reliable across irregular and incomplete records and different hospitals, while evaluating calibration, causal validity, and performance beyond retrospective benchmarks.