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Computational Algorithms for Multi-Omics and Electronic Health Records Data- [electronic resource]
Computational Algorithms for Multi-Omics and Electronic Health Records Data- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101302
- ISBN
- 9798379786960
- DDC
- 574
- 저자명
- Guo, Jia.
- 서명/저자
- Computational Algorithms for Multi-Omics and Electronic Health Records Data - [electronic resource]
- 발행사항
- [S.l.]: : Columbia University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(119 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Wang, Shuang.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Real world data have enhanced healthcare research, improving our understanding of disease progression, aiding in diagnosis, and enabling the development of personalized and targeted treatments. In recent years, multi-omics data and electronic health record (EHR) data have become increasingly available, providing researchers with a wealth of information to analyze. The use of machine learning methods with EHR and multi-omics data has emerged as a promising approach to extract valuable insights from these complex data sources. This dissertation focuses on the development of supervised and unsupervised learning methods, as well as their applications to EHR and multi-omics data, with a particular emphasis on early detection of clinical outcomes and identification of novel cancer subtypes.The first part of the dissertation centers on developing a risk prediction tool using EHR data that enables disease early detection so that preventive treatments can be taken to better manage the disease. For this goal, we developed a similarity-based supervised learning method with two applications to predict end-stage kidney disease (ESKD) and aortic stenosis (AS). In the second part of the dissertation, we expanded our goal to a phenome-wide prediction task and developed a patient representation based deep learning method that is able to predict phenotypes across the phenome. Through a weighting scheme, this approach is conducting tailored disease phenotype prediction computationally efficiently with good prediction performance. In the final part of the dissertation, I shifted the focus with the goal to identify clinical meaningful novel disease subtypes with unsupervised learning methods using multi-omics data. We tackled this goal through integrating multiple patient graphs being generated from multiple omics data with molecular level features for an improved disease subtyping.This dissertation has significantly contributed to the development of data-driven approaches to healthcare and biomedical research using EHR data and multi-omics data. The new methodologies developed with applications in multiple diseases using EHR and multi-omics data advanced our knowledge in disease diagnosis, vulnerable groups identification, and ultimately improve patient care.
- 일반주제명
- Biostatistics.
- 일반주제명
- Bioinformatics.
- 일반주제명
- Health care management.
- 키워드
- Multi-omics data
- 키워드
- ESKD
- 키워드
- Aortic stenosis
- 키워드
- Patient care
- 기타저자
- Columbia University Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101302
■006m o d
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■020 ▼a9798379786960
■035 ▼a(MiAaPQ)AAI30531123
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aGuo, Jia.
■24510▼aComputational Algorithms for Multi-Omics and Electronic Health Records Data▼h[electronic resource]
■260 ▼a[S.l.]:▼bColumbia University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(119 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Wang, Shuang.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aReal world data have enhanced healthcare research, improving our understanding of disease progression, aiding in diagnosis, and enabling the development of personalized and targeted treatments. In recent years, multi-omics data and electronic health record (EHR) data have become increasingly available, providing researchers with a wealth of information to analyze. The use of machine learning methods with EHR and multi-omics data has emerged as a promising approach to extract valuable insights from these complex data sources. This dissertation focuses on the development of supervised and unsupervised learning methods, as well as their applications to EHR and multi-omics data, with a particular emphasis on early detection of clinical outcomes and identification of novel cancer subtypes.The first part of the dissertation centers on developing a risk prediction tool using EHR data that enables disease early detection so that preventive treatments can be taken to better manage the disease. For this goal, we developed a similarity-based supervised learning method with two applications to predict end-stage kidney disease (ESKD) and aortic stenosis (AS). In the second part of the dissertation, we expanded our goal to a phenome-wide prediction task and developed a patient representation based deep learning method that is able to predict phenotypes across the phenome. Through a weighting scheme, this approach is conducting tailored disease phenotype prediction computationally efficiently with good prediction performance. In the final part of the dissertation, I shifted the focus with the goal to identify clinical meaningful novel disease subtypes with unsupervised learning methods using multi-omics data. We tackled this goal through integrating multiple patient graphs being generated from multiple omics data with molecular level features for an improved disease subtyping.This dissertation has significantly contributed to the development of data-driven approaches to healthcare and biomedical research using EHR data and multi-omics data. The new methodologies developed with applications in multiple diseases using EHR and multi-omics data advanced our knowledge in disease diagnosis, vulnerable groups identification, and ultimately improve patient care.
■590 ▼aSchool code: 0054.
■650 4▼aBiostatistics.
■650 4▼aBioinformatics.
■650 4▼aHealth care management.
■653 ▼aElectronic health record
■653 ▼aMulti-omics data
■653 ▼aESKD
■653 ▼aAortic stenosis
■653 ▼aPatient care
■690 ▼a0308
■690 ▼a0769
■690 ▼a0715
■71020▼aColumbia University▼bBiostatistics.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933551▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
