{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T18:41:15Z","timestamp":1705689675825},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"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":[[2022,1,14]]},"abstract":"<jats:p>The goal of this study was to build a machine learning model for early prostate cancer prediction based on healthcare utilization patterns. We examined the frequency and pattern changes of healthcare utilization in 2916 prostate cancer patients 3 years prior to their prostate cancer diagnoses and explored several supervised machine learning techniques to predict possible prostate cancer diagnosis. Analysis of patients\u2019 medical activities between 1 year and 2 years prior to their prostate cancer diagnoses using XGBoost model provided the best prediction accuracy with high F1 score (0.9) and AUC score (0.73). These pilot results indicated that application of machine learning to healthcare utilization patterns may result in early identification of prostate cancer diagnosis.<\/jats:p>","DOI":"10.3233\/shti210860","type":"book-chapter","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:47:52Z","timestamp":1642434472000},"source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns"],"prefix":"10.3233","author":[{"given":"Joseph","family":"Finkelstein","sequence":"first","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]},{"given":"Wanting","family":"Cui","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]},{"given":"Tiphaine C.","family":"Martin","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]},{"given":"Ramon","family":"Parsons","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Informatics and Technology in Clinical Care and Public Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:47:52Z","timestamp":1642434472000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210860","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,14]]}}}