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Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer.

Isaac E Kim, Cecile P G Meier-Scherling, Steve R Zhou, Simon J C Soerensen, Ismail Ajjawi, Benjamin I Chung, Eugene Shkolyar, Geoffrey A Sonn, Joseph C Liao, Michael S Leapman

Urologic oncologyOct 1, 2026PMID 42674923doi:10.1016/j.urolonc.2026.07.015 Journal ArticleComparative StudypubmedProvenance
Source
PubMed
Retrieved
Sep 30, 2026
Layer
normalized (units and labels harmonized; values unchanged)
Run
ING-PUBMED-20260930-000001
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OBJECTIVE: To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess whether these approaches can improve upon established clinical risk prediction models. METHODS: Using the Surveillance, Epidemiology, and End Results…

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