Identifying cohorts at elevated risk of cancers using generative modeling of patient health states
This study introduces GenEHR, an autoregressive generative model trained on electronic health records from millions of patients that explicitly represents irregular inter-visit time intervals via RAdix Time Encoding and combines parameter-efficient gated low-rank adaptation for supervised fine-tuning, improving prediction of a first cancer diagnosis within a five-year horizon across five large EHR cohorts and supporting risk-based screening for aggressive cancers such as pancreatic and ovarian cancer.