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Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer

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개인저자Merdan, Selin.
단체저자명University of Michigan. Industrial & Operations Engineering.
서명/저자사항Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer.
발행사항[S.l.] : University of Michigan., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항155 p.
소장본 주기School code: 0127.
ISBN9780438126091
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Brian Denton.
요약Technological advances in biomarkers and imaging tests are creating new avenues to advance precision health for early detection of cancer. These advances have resulted in multiple layers of information that can be used to make clinical decisions
요약In the first part, we develop and validate predictive models to assess individual PCa risk using known clinical risk factors. Because not all men with newly-diagnosed PCa received imaging at diagnosis, we use an established method to correct for
요약In the second part of this thesis, we combine optimization and machine learning approaches into a robust optimization framework to design imaging guidelines that can account for imperfect calibration of predictions. We investigate efficient and
요약In the third and final part of this thesis, we investigate the optimal design of composite multi-biomarker tests to achieve early detection of prostate cancer. Biomarker tests vary significantly in cost, and cause false positive and false negati
요약In this dissertation, we identify new principles and methods to guide the design of early detection protocols for PCa using new diagnostic technologies. We provide important clinical evidence that can be used to improve health outcomes of patien
일반주제명Industrial engineering.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000511

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1 WE00025153 DP 658 가야대학교/전자책서버(컴퓨터서버)/ 대출불가(별치) 인쇄 이미지  

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