Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine lear...
Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms
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Publisher
London: Nature Publishing Group UK
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Language
English
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Publisher
London: Nature Publishing Group UK
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Contents
Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developi...
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Full title
Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms
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TN_cdi_doaj_primary_oai_doaj_org_article_e48c479748db415b884ead5aa966a8b7
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e48c479748db415b884ead5aa966a8b7
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-89607-8
