{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:13:14Z","timestamp":1782835994469,"version":"3.54.5"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Cancer Institute","award":["K25CA267052"],"award-info":[{"award-number":["K25CA267052"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01869-8","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T17:58:44Z","timestamp":1753120724000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhancing EHR-based pancreatic cancer prediction with LLM-derived embeddings"],"prefix":"10.1038","volume":"8","author":[{"given":"Jiheum","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose M.","family":"Acitores Cortina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin","family":"Hur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Tatonetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"1869_CR1","first-page":"10","volume":"75","author":"RL Siegel","year":"2025","unstructured":"Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H. & Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 75, 10\u201345 (2025).","journal-title":"CA Cancer J. Clin."},{"key":"1869_CR2","unstructured":"American Cancer Society. Cancer Facts & Figures 2021. Atlanta: American Cancer Society; (2021)."},{"key":"1869_CR3","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1001\/jama.2019.10232","volume":"322","author":"DK Owens","year":"2019","unstructured":"Owens, D. K. et al. Screening for pancreatic cancer: US Preventive Services Task Force Reaffirmation Recommendation Statement. JAMA 322, 438\u2013444 (2019).","journal-title":"JAMA"},{"key":"1869_CR4","doi-asserted-by":"publisher","unstructured":"Farr, K. P. et al. Imaging modalities for early detection of pancreatic cancer: current state and future research opportunities. Cancers 14. https:\/\/doi.org\/10.3390\/cancers14102539.","DOI":"10.3390\/cancers14102539"},{"key":"1869_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbcan.2023.188868","volume":"1878","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y. et al. Liquid biopsy in pancreatic cancer - Current perspective and future outlook. Biochim. Biophys. Acta Rev. Cancer 1878, 188868 (2023).","journal-title":"Biochim. Biophys. Acta Rev. Cancer"},{"key":"1869_CR6","doi-asserted-by":"publisher","unstructured":"Dash, S., Shakyawar, S. K., Sharma, M., Kaushik, S. Big data in healthcare: management, analysis and future prospects. J. Big Data-Ger. 6. https:\/\/doi.org\/10.1186\/s40537-019-0217-0.","DOI":"10.1186\/s40537-019-0217-0"},{"key":"1869_CR7","unstructured":"Li, L. et al. A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs). arXiv preprint arXiv:240503066 (2024)."},{"key":"1869_CR8","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s41746-023-00970-0","volume":"7","author":"M Guevara","year":"2024","unstructured":"Guevara, M. et al. Large language models to identify social determinants of health in electronic health records. NPJ Digit Med. 7, 6 (2024).","journal-title":"NPJ Digit Med."},{"key":"1869_CR9","doi-asserted-by":"publisher","unstructured":"Yan, C. et al. Large language models facilitate the generation of electronic health record phenotyping algorithms. J. Am. Med. Inform. Assoc. https:\/\/doi.org\/10.1093\/jamia\/ocae072.","DOI":"10.1093\/jamia\/ocae072"},{"key":"1869_CR10","doi-asserted-by":"publisher","unstructured":"Chen, Y. & Zou, J. GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT. bioRxiv. https:\/\/doi.org\/10.1101\/2023.10.16.562533.","DOI":"10.1101\/2023.10.16.562533"},{"key":"1869_CR11","doi-asserted-by":"publisher","unstructured":"Patterson, J., Tatonetti N. KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy. J. Am. Med. Inform. Assoc. https:\/\/doi.org\/10.1093\/jamia\/ocae155. (2024).","DOI":"10.1093\/jamia\/ocae155"},{"key":"1869_CR12","doi-asserted-by":"crossref","unstructured":"Placido, D. et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med. 29, 1113\u20131122. (2023)","DOI":"10.1038\/s41591-023-02332-5"},{"key":"1869_CR13","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/S0140-6736(20)30540-7","volume":"396","author":"J Malyszko","year":"2020","unstructured":"Malyszko, J., Tesarova, P., Capasso, G. & Capasso, A. The link between kidney disease and cancer: complications and treatment. Lancet 396, 277\u2013287 (2020).","journal-title":"Lancet"},{"key":"1869_CR14","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1111\/hepr.14081","volume":"54","author":"T Sakaue","year":"2024","unstructured":"Sakaue, T., Terabe, H., Takedatsu, H. & Kawaguchi, T. Association between nonalcholic fatty liver disease and pancreatic cancer: Epidemiology, mechanisms, and antidiabetic medication. Hepatol. Res. 54, 729\u2013735 (2024).","journal-title":"Hepatol. Res."},{"key":"1869_CR15","doi-asserted-by":"publisher","unstructured":"Park, J. H., Hong, J. Y., Han, K., Kan,g W., Park, J. K. Increased risk of pancreatic cancer in individuals with non-alcoholic fatty liver disease. Sci Rep-Uk;12, doi: ARTN 10681 https:\/\/doi.org\/10.1038\/s41598-022-14856-w.","DOI":"10.1038\/s41598-022-14856-w"},{"key":"1869_CR16","doi-asserted-by":"publisher","first-page":"77","DOI":"10.6004\/jnccn.2021.0001","volume":"19","author":"MB Daly","year":"2021","unstructured":"Daly, M. B. et al. Genetic\/familial high-risk assessment: breast, ovarian, and pancreatic, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Canc Netw. 19, 77\u2013102 (2021).","journal-title":"J. Natl. Compr. Canc Netw."},{"key":"1869_CR17","doi-asserted-by":"publisher","unstructured":"Ahmadi, N., Peng, Y., Wolfien, M. & Zoch, M., Sedlmayr, M. OMOP CDM can facilitate data-driven studies for cancer prediction: a systematic review. Int. J. Mol. Sci. 23, doi: ARTN 11834 https:\/\/doi.org\/10.3390\/ijms231911834. (2022).","DOI":"10.3390\/ijms231911834"},{"key":"1869_CR18","unstructured":"Subramanian, V. Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch: Packt Publishing Ltd. (2018)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01869-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01869-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01869-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:02:57Z","timestamp":1760486577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01869-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,21]]},"references-count":18,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1869"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01869-8","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,21]]},"assertion":[{"value":"30 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The wrong Supplementary file was originally published with this article; it has now been replaced with the correct. The original article has been corrected.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"465"}}