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Actualizing Impact of AI in Public Health: Optimization of Scarce Health Intervention Resources in the Real World- [electronic resource]
Actualizing Impact of AI in Public Health: Optimization of Scarce Health Intervention Resources in the Real World- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101245
- ISBN
- 9798380848817
- DDC
- 004
- 서명/저자
- Actualizing Impact of AI in Public Health: Optimization of Scarce Health Intervention Resources in the Real World - [electronic resource]
- 발행사항
- [S.l.]: : Harvard University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(221 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
- 주기사항
- Advisor: Tambe, Milind.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약While AI is assuming omnipresence today more than ever, its adoption is still limited in solving challenges pertaining to socially-critical problem domains such as in public health, especially among low-resource and underserved communities. Motivated by the desire to solve impactful, real-world problems that involve reasoning, strategic decision-making, or planning in uncertain, stochastic or resource-limited settings, my thesis presents novel solutions designed for two such real-world public health challenges: tuberculosis prevention and improving maternal and child healthcare.Building AI systems for realizing social impact in public health, demands solving a number of fundamental research questions. For instance, community health workers and NGOs operating with limited health resources face the challenge of optimally utilizing these resources to maximize their impact. In doing so, such NGOs must account for domain-specific considerations such as fairness or risk-averseness and plan the limited resources to serve beneficiaries at scale, in an uncertain and dynamically changing world. Even with new solution techniques for allocating limited resources in such socially critical domains now being built, their accurate evaluation through Randomized Controlled Trials (RCTs) remains difficult due to high sample variance in these settings.Towards tackling these challenges, my thesis utilizes techniques such as Restless Multi-Armed Bandits (RMAB) to solve the sequential decision-making problem of allocating scarce health intervention resources. My thesis builds computationally efficient solution algorithms to this problem, that can be adopted by non-profits without needing access to heavy computing power. Next, I also propose techniques that allow the planner to accommodate real-world considerations such as risk-averseness or fairness in planning health interventions. Furthermore, my thesis also builds solutions that can plan such health interventions while accounting for dynamically changing patient cohorts and the finite stay of patients in such health programs. Transcending the boundaries of traditional research, I have transitioned this work from the blackboard to a first-of-its-kind field evaluation of the RMAB algorithm, involving 23,000 real-world mothers over a 7-week period, results of which show a ∼ 30% improvement in the performance metric of interest. Finally, my work mitigates the challenges faced in the evaluation of such resource allocation algorithms through RCTs. Using techniques from causal reasoning, I present a novel concept that retrospectively reassigns participants to experimental groups in a trial. Using this concept, I build a new estimator, that I show, can sharply reduce sample variance.
- 일반주제명
- Computer science.
- 일반주제명
- Public health.
- 키워드
- AI systems
- 키워드
- Restless bandits
- 기타저자
- Harvard University Engineering and Applied Sciences - Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-05B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101245
■006m o d
■007cr#unu||||||||
■020 ▼a9798380848817
■035 ▼a(MiAaPQ)AAI30529119
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aMate, Aditya Shrikant.▼0(orcid)0009-0008-7324-3990
■24510▼aActualizing Impact of AI in Public Health: Optimization of Scarce Health Intervention Resources in the Real World▼h[electronic resource]
■260 ▼a[S.l.]:▼bHarvard University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(221 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-05, Section: B.
■500 ▼aAdvisor: Tambe, Milind.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aWhile AI is assuming omnipresence today more than ever, its adoption is still limited in solving challenges pertaining to socially-critical problem domains such as in public health, especially among low-resource and underserved communities. Motivated by the desire to solve impactful, real-world problems that involve reasoning, strategic decision-making, or planning in uncertain, stochastic or resource-limited settings, my thesis presents novel solutions designed for two such real-world public health challenges: tuberculosis prevention and improving maternal and child healthcare.Building AI systems for realizing social impact in public health, demands solving a number of fundamental research questions. For instance, community health workers and NGOs operating with limited health resources face the challenge of optimally utilizing these resources to maximize their impact. In doing so, such NGOs must account for domain-specific considerations such as fairness or risk-averseness and plan the limited resources to serve beneficiaries at scale, in an uncertain and dynamically changing world. Even with new solution techniques for allocating limited resources in such socially critical domains now being built, their accurate evaluation through Randomized Controlled Trials (RCTs) remains difficult due to high sample variance in these settings.Towards tackling these challenges, my thesis utilizes techniques such as Restless Multi-Armed Bandits (RMAB) to solve the sequential decision-making problem of allocating scarce health intervention resources. My thesis builds computationally efficient solution algorithms to this problem, that can be adopted by non-profits without needing access to heavy computing power. Next, I also propose techniques that allow the planner to accommodate real-world considerations such as risk-averseness or fairness in planning health interventions. Furthermore, my thesis also builds solutions that can plan such health interventions while accounting for dynamically changing patient cohorts and the finite stay of patients in such health programs. Transcending the boundaries of traditional research, I have transitioned this work from the blackboard to a first-of-its-kind field evaluation of the RMAB algorithm, involving 23,000 real-world mothers over a 7-week period, results of which show a ∼ 30% improvement in the performance metric of interest. Finally, my work mitigates the challenges faced in the evaluation of such resource allocation algorithms through RCTs. Using techniques from causal reasoning, I present a novel concept that retrospectively reassigns participants to experimental groups in a trial. Using this concept, I build a new estimator, that I show, can sharply reduce sample variance.
■590 ▼aSchool code: 0084.
■650 4▼aComputer science.
■650 4▼aPublic health.
■653 ▼aAI systems
■653 ▼aIntervention planning
■653 ▼aLimited resources
■653 ▼aReinforcement learning
■653 ▼aRestless bandits
■653 ▼aSequential decision making
■690 ▼a0800
■690 ▼a0984
■690 ▼a0573
■71020▼aHarvard University▼bEngineering and Applied Sciences - Computer Science.
■7730 ▼tDissertations Abstracts International▼g85-05B.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933429▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
