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Machine Learning for Data-driven Biomedical Decision Making

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자료유형E-Book
개인저자Luckett, Daniel J.
단체저자명The University of North Carolina at Chapel Hill. Biostatistics.
서명/저자사항Machine Learning for Data-driven Biomedical Decision Making.
발행사항[S.l.] : The University of North Carolina at Chapel Hill., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항152 p.
소장본 주기School code: 0153.
ISBN9780438063235
일반주기 Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Michael R. Kosorok.
요약The big data age has brought with it challenges and opportunities for biomedical decision making. New technologies allow for collecting large data sets that can be used to tailor treatment. In this dissertation, we develop machine learning metho
요약Many problems in biomedical decision making can be expressed as classification problems. The costs of false positives and false negatives differ across application domains and this trade-off is often displayed using a receiver operating characte
요약Precision medicine is the paradigm of incorporating individual patient factors into treatment decisions, formalized through individualized treatment regimes (ITR's), or maps from the covariate space into the treatment space. The optimal ITR is d
요약Clinical decision making often requires balancing trade-offs between multiple outcomes while accounting for patient preferences, creating a disconnect with the traditional definition of the optimal ITR. If an instrument to elicit patient prefere
요약Direct search methods, such as outcome weighted learning (OWL), estimate the optimal ITR by maximizing an inverse probability weighted estimator (IPWE) over a class of ITR's. In the final chapter, we show that the IPWE objective function is a pr
일반주제명Biostatistics.
언어영어
기본자료 저록Dissertation Abstracts International79-10B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14997419

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