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

• URL: http://arxiv.org/abs/2508.06627v3
• Date: Mon, 18 Aug 2025 23:48:23 GMT
• Title: Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records
• Authors: Mosbah Aouad, Anirudh Choudhary, Awais Farooq, Steven Nevers, Lusine Demirkhanyan, Bhrandon Harris, Suguna Pappu, Christopher Gondi, Ravishankar Iyer,
• Abstract summary: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers.<n>Early detection remains a major clinical challenge.<n>We develop and evaluate our approach on a real-world dataset of nearly 4,700 patients.
• Score: 0.7770975325131922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, and early detection remains a major clinical challenge due to the absence of specific symptoms and reliable biomarkers. In this work, we propose a new multimodal approach that integrates longitudinal diagnosis code histories and routinely collected laboratory measurements from electronic health records to detect PDAC up to one year prior to clinical 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 interactions between the two modalities. We develop and evaluate our approach on a real-world dataset 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. Our code is available at https://github.com/MosbahAouad/EarlyPDAC-MML.

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