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Developing clinical prediction models (e.g., mortality prediction) based onelectronic health records (EHRs) typically relies on expert opinion for featureselection and adjusting observation window size. This burdens experts andcreates a bottleneck in the development process. We propose Retrieval-EnhancedMedical prediction model (REMed) to address such challenges. REMed canessentially evaluate an unlimited number of clinical events, select therelevant ones, and make predictions. This approach effectively eliminates theneed for manual feature selection and enables an unrestricted observationwindow. We verified these properties through experiments on 27 clinical tasksand two independent cohorts from publicly available EHR datasets, where REMedoutperformed other contemporary architectures that aim to handle as many eventsas possible. Notably, we found that the preferences of REMed align closely withthose of medical experts. We expect our approach to significantly expedite thedevelopment of EHR prediction models by minimizing clinicians' need for manualinvolvement.

General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History