서브메뉴
검색
Predicting Depression in Black Women: A Machine Learning Epigenetic Approach
Predicting Depression in Black Women: A Machine Learning Epigenetic Approach
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152802
- ISBN
- 9798383701737
- DDC
- 610.73
- 서명/저자
- Predicting Depression in Black Women: A Machine Learning Epigenetic Approach
- 발행사항
- [Sl] : Columbia University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 213 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: A.
- 주기사항
- Advisor: Masterson Creber, Ruth.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2024.
- 초록/해제
- 요약Depression is one of the most widespread and disabling mental health disorders affecting adults worldwide, and Black women bear a disproportionate burden of this disorder. With its varied symptom presentation, depression can be difficult to diagnose. In addition, Black women may be less likely to report symptoms due to cultural stigma. The purpose of this dissertation is to examine the associations between social determinants of health and depressive symptoms using DNA methylation data and machine learning to predict depressive symptoms in Black women. Chapter 2 contains two comprehensive literature reviews: a scoping review of machine learning methods used to analyze omics data to classify depressed cases and healthy controls and a concept analysis of depression in Black mothers. Chapter 3 examines associations between social determinants of health, depressive symptoms, and DNA methylation. Chapter 3A focuses on socioeconomic deprivation; Chapter 3B focuses on perceived income inadequacy; and Chapter 3C identifies differential methylation associated with depressive symptoms. Chapter 4 utilizes supervised machine learning algorithms to predict depressive symptoms and perform feature selection. These chapters show the harmful effects that perceived discrimination can have on the mental health of Black women. Additionally, the results indicate that DNA methylation is associated with depressive symptoms, an area which requires further research.
- 일반주제명
- Nursing
- 일반주제명
- Bioinformatics
- 일반주제명
- Mental health
- 일반주제명
- Womens studies
- 일반주제명
- Clinical psychology
- 키워드
- Black women
- 키워드
- Depression
- 키워드
- Epigenetics
- 키워드
- Machine learning
- 기타저자
- Columbia University Nursing
- 기본자료저록
- Dissertations Abstracts International. 86-02A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017163864
■00520250211152802
■006m o d
■007cr#unu||||||||
■020 ▼a9798383701737
■035 ▼a(MiAaPQ)AAI31556773
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610.73
■1001 ▼aTaylor, Brittany N.
■24510▼aPredicting Depression in Black Women: A Machine Learning Epigenetic Approach
■260 ▼a[Sl]▼bColumbia University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a213 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: A.
■500 ▼aAdvisor: Masterson Creber, Ruth.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2024.
■520 ▼aDepression is one of the most widespread and disabling mental health disorders affecting adults worldwide, and Black women bear a disproportionate burden of this disorder. With its varied symptom presentation, depression can be difficult to diagnose. In addition, Black women may be less likely to report symptoms due to cultural stigma. The purpose of this dissertation is to examine the associations between social determinants of health and depressive symptoms using DNA methylation data and machine learning to predict depressive symptoms in Black women. Chapter 2 contains two comprehensive literature reviews: a scoping review of machine learning methods used to analyze omics data to classify depressed cases and healthy controls and a concept analysis of depression in Black mothers. Chapter 3 examines associations between social determinants of health, depressive symptoms, and DNA methylation. Chapter 3A focuses on socioeconomic deprivation; Chapter 3B focuses on perceived income inadequacy; and Chapter 3C identifies differential methylation associated with depressive symptoms. Chapter 4 utilizes supervised machine learning algorithms to predict depressive symptoms and perform feature selection. These chapters show the harmful effects that perceived discrimination can have on the mental health of Black women. Additionally, the results indicate that DNA methylation is associated with depressive symptoms, an area which requires further research.
■590 ▼aSchool code: 0054.
■650 4▼aNursing
■650 4▼aBioinformatics
■650 4▼aMental health
■650 4▼aWomens studies
■650 4▼aClinical psychology
■653 ▼aBlack women
■653 ▼aDepression
■653 ▼aEpigenetics
■653 ▼aMachine learning
■653 ▼aSocial determinants of health
■690 ▼a0569
■690 ▼a0715
■690 ▼a0453
■690 ▼a0622
■690 ▼a0347
■71020▼aColumbia University▼bNursing.
■7730 ▼tDissertations Abstracts International▼g86-02A.
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163864▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
