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Novel Sequential Decision-Making Strategies in Healthcare Settings- [electronic resource]
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Novel Sequential Decision-Making Strategies in Healthcare Settings- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100121
- ISBN
- 9798379755881
- DDC
- 004
- 서명/저자
- Novel Sequential Decision-Making Strategies in Healthcare Settings - [electronic resource]
- 발행사항
- [S.l.]: : University of Pennsylvania., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(192 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Bastani, Hamsa Sridhar.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약There are currently many good methods for solving sequential decision-making problems under certain assumptions on the structure of the incoming data. However, when the avail- able data fails to meet such assumptions, such as when it involves surrogate or proxy signals, involves measuring survival times, or is unstructured such as in the case of image data, these methods fall short. In this dissertation, we aim to address this discrepancy for specific types of complex data, inspired by healthcare settings in which we might encounter such data. We introduce new methods to better learn from incoming and limited data sources in order to make more efficient decisions.The goal of this work is to develop methods that medical professionals can use to better leverage data in order to determine efficacy or necessity of treatment. All three projects included in this dissertation focus on developing adaptive methods that leverage information as it becomes available to update predictions, to ensure that medical professionals can better balance providing effective healthcare with using available resources efficiently. We also focus on quantifying the performance of these algorithms, and identifying the conditions under which it is most beneficial to use these novel strategies instead of currently employed methods.
- 일반주제명
- Computer science.
- 기타저자
- University of Pennsylvania Operations Information and Decisions
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798379755881
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aAnderer, Arielle Elissa.
■24510▼aNovel Sequential Decision-Making Strategies in Healthcare Settings▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Pennsylvania. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(192 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Bastani, Hamsa Sridhar.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThere are currently many good methods for solving sequential decision-making problems under certain assumptions on the structure of the incoming data. However, when the avail- able data fails to meet such assumptions, such as when it involves surrogate or proxy signals, involves measuring survival times, or is unstructured such as in the case of image data, these methods fall short. In this dissertation, we aim to address this discrepancy for specific types of complex data, inspired by healthcare settings in which we might encounter such data. We introduce new methods to better learn from incoming and limited data sources in order to make more efficient decisions.The goal of this work is to develop methods that medical professionals can use to better leverage data in order to determine efficacy or necessity of treatment. All three projects included in this dissertation focus on developing adaptive methods that leverage information as it becomes available to update predictions, to ensure that medical professionals can better balance providing effective healthcare with using available resources efficiently. We also focus on quantifying the performance of these algorithms, and identifying the conditions under which it is most beneficial to use these novel strategies instead of currently employed methods.
■590 ▼aSchool code: 0175.
■650 4▼aComputer science.
■653 ▼aAdaptive learning algorithms
■653 ▼aHealthcare analytics
■653 ▼aSequential decision making
■653 ▼aHealthcare settings
■690 ▼a0796
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of Pennsylvania▼bOperations, Information and Decisions.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931811▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
