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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
- Control Number
- 0017162304
- International Standard Book Number
- 9798382833071
- Dewey Decimal Classification Number
- 658
- Main Entry-Personal Name
- Zhang, Suyanpeng.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of Southern California., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 170 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- General Note
- Advisor: Suen, Sze-Chuan.
- Dissertation Note
- Thesis (Ph.D.)--University of Southern California, 2024.
- Summary, Etc.
- 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.
- Subject Added Entry-Topical Term
- Industrial engineering.
- Subject Added Entry-Topical Term
- Medicine.
- Index Term-Uncontrolled
- Dynamic programming
- Index Term-Uncontrolled
- Infectious disease control
- Index Term-Uncontrolled
- Liver transplant
- Index Term-Uncontrolled
- Healthcare settings
- Index Term-Uncontrolled
- Decision-making
- Added Entry-Corporate Name
- University of Southern California Industrial and Systems Engineering
- Host Item Entry
- Dissertations Abstracts International. 85-12B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:654866
MARC
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■1001 ▼aZhang, Suyanpeng.
■24510▼aOptimizing Healthcare Decision-Making: Markov Decision Processes for Liver Transplants, Frequent Interventions, and Infectious Disease Control.
■260 ▼a[S.l.]▼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.
