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Phenotyping With Partially Labeled, Partially Observed Data- [electronic resource]
![Phenotyping With Partially Labeled, Partially Observed Data - [electronic resource]](/Users/Baul/Images/book.png)
Phenotyping With Partially Labeled, Partially Observed Data- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101916
- ISBN
- 9798380588379
- DDC
- 574
- 서명/저자
- Phenotyping With Partially Labeled, Partially Observed Data - [electronic resource]
- 발행사항
- [S.l.]: : Columbia University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(133 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
- 주기사항
- Advisor: Perotte, Adler.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Identifying a group of individuals that share a common set of characteristics is a conceptually simple task, which is often difficult in practice. Such phenotyping problems emerge in various settings, including the analysis of clinical data. In this setting, phenotyping is often stymied by persistent data quality issues. These include a lack of reliable labels to indicate the presence of absence of characteristics of interest, and significant missingness in observed variables. This dissertation introduces methods for learning phenotypes when the data contain missing values (partially observed) and labels are scarce (partially labeled). Aim 1 utilizes an unsupervised probabilistic graphical model to learn phenotypes from partially observed data. Aim 2 introduces a related semi-supervised probabilistic graphical model for learning phenotypes from partially labeled clinical data. Finally, Aim 3 describes a method for training deep generative models when the training data contain missing values. The algorithm is then applied in a semi-supervised setting where it accounts for partially labeled data as well.
- 일반주제명
- Bioinformatics.
- 일반주제명
- Computer engineering.
- 일반주제명
- Biomedical engineering.
- 키워드
- Graphical models
- 키워드
- Machine learning
- 키워드
- Phenotyping
- 기타저자
- Tatonetti, Nicholas
- 기타저자
- Columbia University Biomedical Informatics
- 기본자료저록
- Dissertations Abstracts International. 85-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101916
■006m o d
■007cr#unu||||||||
■020 ▼a9798380588379
■035 ▼a(MiAaPQ)AAI30688278
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aRodriguez, Victor A.
■24510▼aPhenotyping With Partially Labeled, Partially Observed Data▼h[electronic resource]
■260 ▼a[S.l.]:▼bColumbia University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(133 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-04, Section: B.
■500 ▼aAdvisor: Perotte, Adler.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aIdentifying a group of individuals that share a common set of characteristics is a conceptually simple task, which is often difficult in practice. Such phenotyping problems emerge in various settings, including the analysis of clinical data. In this setting, phenotyping is often stymied by persistent data quality issues. These include a lack of reliable labels to indicate the presence of absence of characteristics of interest, and significant missingness in observed variables. This dissertation introduces methods for learning phenotypes when the data contain missing values (partially observed) and labels are scarce (partially labeled). Aim 1 utilizes an unsupervised probabilistic graphical model to learn phenotypes from partially observed data. Aim 2 introduces a related semi-supervised probabilistic graphical model for learning phenotypes from partially labeled clinical data. Finally, Aim 3 describes a method for training deep generative models when the training data contain missing values. The algorithm is then applied in a semi-supervised setting where it accounts for partially labeled data as well.
■590 ▼aSchool code: 0054.
■650 4▼aBioinformatics.
■650 4▼aComputer engineering.
■650 4▼aBiomedical engineering.
■653 ▼aDeep generative models
■653 ▼aGraphical models
■653 ▼aMachine learning
■653 ▼aPhenotyping
■690 ▼a0715
■690 ▼a0464
■690 ▼a0541
■70010▼aTatonetti, Nicholas▼ejoint author
■71020▼aColumbia University▼bBiomedical Informatics.
■7730 ▼tDissertations Abstracts International▼g85-04B.
■773 ▼tDissertation Abstract International
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935303▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
