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Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer. |
| 개인저자 | Merdan, Selin. |
| 단체저자명 | University of Michigan. Industrial & Operations Engineering. |
| 발행사항 | [S.l.]: University of Michigan., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 155 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438126091 |
| 학위논문주기 | Thesis (Ph.D.)--University of Michigan, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Brian Denton. |
| 요약 | Technological 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 |
| 요약 | In 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 |
| 요약 | In 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 |
| 요약 | In 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 |
| 요약 | In 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 |
| 일반주제명 | Industrial engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
