{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T14:10:06Z","timestamp":1744121406836,"version":"3.40.3"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685908","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,8]]},"abstract":"<jats:p>Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers for predicting survival in patients with metastatic castration-resistant prostate cancer (mCRPC) still has space for improvement. This study aimed to predict 3-year survival in mCRPC patients by analyzing clinical and demographic features. A total of 664 patients with 41 clinical and demographic variables were evaluated. We utilized the class-weighted XGBoost algorithm to address class imbalance and improve the accuracy of outcome predictions. The model achieved an accuracy of 0.73, an AUC of 0.74, a recall, precision and F1 score value of 0.84, indicating a good ability to distinguish between patients who survived less than or more than 3 years. Our findings suggest that PSA, along with other non-specific biomarkers such as albumin and LDH, are significant predictors of survival in mCRPC patients and can be successfully used in machine learning algorithms to predict survival in mCRPC patients.<\/jats:p>","DOI":"10.3233\/shti250071","type":"book-chapter","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T13:30:27Z","timestamp":1744119027000},"source":"Crossref","is-referenced-by-count":0,"title":["Using Machine Learning to Predict Survival in Patients with Metastatic Castration-Resistant Prostate Cancer"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-0292","authenticated-orcid":false,"given":"Xingyue","family":"Huo","sequence":"first","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manish","family":"Kohli","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Envisioning the Future of Health Informatics and Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T13:30:28Z","timestamp":1744119028000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"ISBN":["9781643685908"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250071","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]}}}