Generative model of patient health states and pan-cancer risk stratification

Abstract

While large language models are powerful generators of new text, forecasting disease progression from longitudinal health histories remains a challenging problem. We introduce GenEHR, an autoregressive generative model trained on electronic health records (EHRs) from millions of patients that explicitly represents the irregular time intervals between visits when forecasting future clinical events. We combine the general-purpose patient representation learned during foundational training with parameter-efficient supervised adaptation for the task of pan-cancer risk stratification. In five large EHR cohorts supervised adaptation substantially improved prediction performance of a first cancer diagnosis within a five year horizon window. Our retrospective results support the evaluation of GenEHR-CancerRisk as a prospective clinical decision-support tool for prioritizing patients for risk-based screening for aggressive cancer types, such as pancreatic and ovarian cancer.