Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest c ancers, and early detection remains a major clinical challenge due to the absence of spec ific symptoms and reliable biomarkers. In this work, we propose a new multimodal appro ach that integrates longitudinal diagnosis code histories and routinely collected laborato ry measurements from electronic health records to detect PDAC up to one year prior to clin ical diagnosis. Our method combines neural controlled differential equations to model irregular lab time series, pretrained language models and recurrent networks to learn diagnosis code trajectory representations, and cross-attention mechanisms to capture in teractions between the two modalities. We develop and evaluate our approach on a real-world dat aset of nearly 4,700 patients and achieve significant improvements in AUC ranging from 6.5 % to 15.5% over state-of-the-art methods. Furthermore, our model identifies diagnosis codes and laboratory panels associated with elevated PDAC risk, including both established and new biomarkers.