Enhancing EHR-based pancreatic cancer prediction with LLM-derived embeddings.

ABSTRACT: Pancreatic cancer (PC) is often diagnosed late, as early symptoms and effective screening tools are lacking, and genetic or familial factors explain only ~10% of cases. Leveraging longitudinal electronic health record (EHR) data may offer a promising avenue for early detection. We developed a predictive model using large language model (LLM)-derived embeddings of medical condition names to enhance learning from EHR data. Across two sites-Columbia University Medical Center and Cedars-Sinai Medical Center-LLM embeddings improved 6-12 month prediction AUROCs from 0.60 to 0.67 and 0.82 to 0.86, respectively. Excluding data from 0-3 months before diagnosis further improved AUROCs to 0.82 and 0.89. Our model achieved a higher positive predictive value (0.141) than using traditional risk factors

SUBMITTER: Park J 

PROVIDER: S-EPMC12280092 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature