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Machine Learning and Statistical Approaches for Efficient Disease Screening
Machine Learning and Statistical Approaches for Efficient Disease Screening
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152745
- ISBN
- 9798342109321
- DDC
- 519
- 서명/저자
- Machine Learning and Statistical Approaches for Efficient Disease Screening
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 155 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Lall, Sanjay;Sadigh, Dorsa.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약We study probabilistic models with algebraic group symmetry and associated algorithms. We are motivated by pooled testing, which is widely used to conserve resources when performing large-scale population screening of infectious diseases such as that caused by SARS-CoV-2. Looking forward, such protocols are also a promising route to increase public access to novel and expensive biological assays in areas like early cancer detection. Classical approaches, however, are based on single-parameter models of disease prevalence and justified under assumptions of independence. We analyze symmetric probabilistic models that allow for correlation in test statuses and accommodate side information. By leveraging the probabilistic symmetries, we give efficient algorithms for learning these models from data and computing the optimal testing designs. When applied to data from the COVID-19 pandemic, these methods indicate more efficient test designs and help explain the unexpectedly high empirical efficiency observed by the original investigators.
- 일반주제명
- Linear programming
- 일반주제명
- Dynamic programming
- 일반주제명
- Medical screening
- 일반주제명
- Optimization techniques
- 일반주제명
- Visualization
- 일반주제명
- Orbits
- 일반주제명
- Symmetry
- 일반주제명
- COVID-19
- 일반주제명
- Computer science
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798342109321
■035 ▼a(MiAaPQ)AAI31520277
■035 ▼a(MiAaPQ)Stanfordhb623pm4894
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aLandolfi, Nicholas Charles.
■24510▼aMachine Learning and Statistical Approaches for Efficient Disease Screening
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a155 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Lall, Sanjay;Sadigh, Dorsa.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aWe study probabilistic models with algebraic group symmetry and associated algorithms. We are motivated by pooled testing, which is widely used to conserve resources when performing large-scale population screening of infectious diseases such as that caused by SARS-CoV-2. Looking forward, such protocols are also a promising route to increase public access to novel and expensive biological assays in areas like early cancer detection. Classical approaches, however, are based on single-parameter models of disease prevalence and justified under assumptions of independence. We analyze symmetric probabilistic models that allow for correlation in test statuses and accommodate side information. By leveraging the probabilistic symmetries, we give efficient algorithms for learning these models from data and computing the optimal testing designs. When applied to data from the COVID-19 pandemic, these methods indicate more efficient test designs and help explain the unexpectedly high empirical efficiency observed by the original investigators.
■590 ▼aSchool code: 0212.
■650 4▼aLinear programming
■650 4▼aDynamic programming
■650 4▼aMedical screening
■650 4▼aOptimization techniques
■650 4▼aVisualization
■650 4▼aOrbits
■650 4▼aSymmetry
■650 4▼aCOVID-19
■650 4▼aComputer science
■690 ▼a0984
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163723▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
