Pancreatic cancer risk prediction using deep sequential modeling of longitudinal diagnostic and medication records.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is a rare, aggressive cancer often diagnosed late with low survival rates, due to the lack of population-wide screening programs and the high cost of early detection methods. To enable early detection of high-risk individuals, we develop a transformer-based model trained on longitudinal Veterans Affairs electronic health record (EHR) with 19,426 PDAC cases and ∼15.9 million controls. Our model combines diagnostic and medication trajectories to predict PDAC risk within a 6-, 12-, and 36-month assessment window. Incorporating medication significantly improved performance; among the top 1,000-5,000 highest-risk patients in a cohort of 1 million patients, 3-year PDAC incidence is 115-70 times higher than a reference estimate based on age and sex alone. F

SUBMITTER: Zheng C 

PROVIDER: S-EPMC12490214 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature