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<p>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 (0.004), and identified many PC patients without these risk factors or known genetic variants. These findings suggest that the EHR-based model may serve as an independent approach for identifying high-risk individuals.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月21日

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  • Jiheum Park,
  • Jason Patterson,
  • Jose M. Acitores Cortina,
  • Tian Gu,
  • Chin Hur,
  • Nicholas Tatonetti

摘要

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 (0.004), and identified many PC patients without these risk factors or known genetic variants. These findings suggest that the EHR-based model may serve as an independent approach for identifying high-risk individuals.