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Optimizing Healthcare Decision-Making: Markov Decision Processes for Liver Transplants, Frequent Interventions, and Infectious Disease Control
Optimizing Healthcare Decision-Making: Markov Decision Processes for Liver Transplants, Frequent Interventions, and Infectious Disease Control
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151957
- ISBN
- 9798382833071
- DDC
- 658
- 서명/저자
- Optimizing Healthcare Decision-Making: Markov Decision Processes for Liver Transplants, Frequent Interventions, and Infectious Disease Control
- 발행사항
- [Sl] : University of Southern California, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 170 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Suen, Sze-Chuan.
- 학위논문주기
- Thesis (Ph.D.)--University of Southern California, 2024.
- 초록/해제
- 요약Repeated decision-making problems in the context of uncertainty naturally arise in healthcare settings. Markov decision processes (MDPs) have proven useful in many healthcare contexts, integrating disease progression, decision-making, costs, and benefits into an optimization framework. However, implementing MDPs in healthcare settings is nontrivial due to challenges including incorporating unique characteristics of certain diseases, determining the optimal frequency of decision-making, and dealing with the infinite number of possible states.In this dissertation, we focus on specific healthcare problems and identify key structural properties to address healthcare questions. We present a finite horizon MDP framework for patients with acute liver failure in need of a transplant, determining the optimal timing for accepting a suboptimal organ to maximize one-year survival probability. Additionally, we study the value provided by having additional decision-making opportunities in each epoch. We provide structural properties of the optimal policies and quantify the difference in optimal values between MDP problems of different decision-making frequencies. We analyze a numerical example using liver transplantation in high-risk patients and treatment initiation for chronic kidney disease patients to illustrate our findings. Finally, in the fourth chapter, to address the curse of dimensionality, we propose a novel greedy algorithm for non-uniform discretization in a population-level MDP for infectious disease control.The dissertation contributes to the field of healthcare applications by providing practical MDP frame-works and efficient algorithms to tackle complex decision-making problems. The theoretical results and empirical analyses offer valuable guidance for healthcare decision-makers in diverse scenarios.
- 일반주제명
- Industrial engineering
- 일반주제명
- Medicine
- 키워드
- Liver transplant
- 키워드
- Decision-making
- 기타저자
- University of Southern California Industrial and Systems Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382833071
■035 ▼a(MiAaPQ)AAI31329250
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aZhang, Suyanpeng.
■24510▼aOptimizing Healthcare Decision-Making: Markov Decision Processes for Liver Transplants, Frequent Interventions, and Infectious Disease Control
■260 ▼a[Sl]▼bUniversity of Southern California▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a170 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Suen, Sze-Chuan.
■5021 ▼aThesis (Ph.D.)--University of Southern California, 2024.
■520 ▼aRepeated decision-making problems in the context of uncertainty naturally arise in healthcare settings. Markov decision processes (MDPs) have proven useful in many healthcare contexts, integrating disease progression, decision-making, costs, and benefits into an optimization framework. However, implementing MDPs in healthcare settings is nontrivial due to challenges including incorporating unique characteristics of certain diseases, determining the optimal frequency of decision-making, and dealing with the infinite number of possible states.In this dissertation, we focus on specific healthcare problems and identify key structural properties to address healthcare questions. We present a finite horizon MDP framework for patients with acute liver failure in need of a transplant, determining the optimal timing for accepting a suboptimal organ to maximize one-year survival probability. Additionally, we study the value provided by having additional decision-making opportunities in each epoch. We provide structural properties of the optimal policies and quantify the difference in optimal values between MDP problems of different decision-making frequencies. We analyze a numerical example using liver transplantation in high-risk patients and treatment initiation for chronic kidney disease patients to illustrate our findings. Finally, in the fourth chapter, to address the curse of dimensionality, we propose a novel greedy algorithm for non-uniform discretization in a population-level MDP for infectious disease control.The dissertation contributes to the field of healthcare applications by providing practical MDP frame-works and efficient algorithms to tackle complex decision-making problems. The theoretical results and empirical analyses offer valuable guidance for healthcare decision-makers in diverse scenarios.
■590 ▼aSchool code: 0208.
■650 4▼aIndustrial engineering
■650 4▼aMedicine
■653 ▼aDynamic programming
■653 ▼aInfectious disease control
■653 ▼aLiver transplant
■653 ▼aHealthcare settings
■653 ▼aDecision-making
■690 ▼a0546
■690 ▼a0796
■690 ▼a0564
■690 ▼a0769
■71020▼aUniversity of Southern California▼bIndustrial and Systems Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0208
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162304▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
