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

arXiv:2310.20204v3 Announce Type: replace
Abstract: Developing clinical prediction models (e.g., mortality prediction) based on electronic health records (EHRs) typically relies on expert opinion for feature selection and adjusting observation window size. This burdens experts and creates a bottleneck in the development process. We propose Retrieval-Enhanced Medical prediction model (REMed) to address such challenges. REMed can essentially evaluate an unlimited number of clinical events, select the relevant ones, and make predictions. This approach effectively eliminates the need for manual feature selection …

abstract adjusting arxiv clinical cs.cl cs.lg development electronic electronic health records expert experts feature feature selection general health history medical mortality near observation opinion prediction prediction models process records retrieval type