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Robust Methods for Clinical Text Classification and Disease Understanding With NLP Extracted Symptoms From Clinical Notes
Robust Methods for Clinical Text Classification and Disease Understanding With NLP Extracted Symptoms From Clinical Notes
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152132
- ISBN
- 9798384094524
- DDC
- 020
- 저자명
- Zhou, Weipeng.
- 서명/저자
- Robust Methods for Clinical Text Classification and Disease Understanding With NLP Extracted Symptoms From Clinical Notes
- 발행사항
- [Sl] : University of Washington, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 134 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Yetisgen, Meliha.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2024.
- 초록/해제
- 요약Electronic Health Records (EHR) contain comprehensive medical and treatment histories of patients and have the potential to be used to provide better healthcare. A significant portion of the EHR is in the form of clinical notes and Natural Language Processing (NLP) methods can help extract hidden information from them. However, applying NLP in healthcare has challenges. Many of the clinical note datasets are scarce and imbalanced, making it difficult to develop generalizable and robust NLP methods. Additionally, effective use of NLP in healthcare requires close collaboration with medical experts to identify and understand meaningful clinical problems. This dissertation addresses these challenges and explores the application of NLP in healthcare. In Chapter 3 and 4, we develop generalizable and robust NLP methods for clinical note classification and female suicide report coding. In Chapter 5 and 6, we apply NLP to extract symptoms from clinical notes and study risk factors associated with out-of-hospital cardiac arrest (OHCA) and Long COVID.
- 일반주제명
- Information science
- 일반주제명
- Computer science
- 일반주제명
- Medicine
- 일반주제명
- Bioinformatics
- 키워드
- Clinical notes
- 기타저자
- University of Washington Biomedical Informatics and Medical Education
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384094524
■035 ▼a(MiAaPQ)AAI31483540
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a020
■1001 ▼aZhou, Weipeng.
■24510▼aRobust Methods for Clinical Text Classification and Disease Understanding With NLP Extracted Symptoms From Clinical Notes
■260 ▼a[Sl]▼bUniversity of Washington▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a134 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Yetisgen, Meliha.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2024.
■520 ▼aElectronic Health Records (EHR) contain comprehensive medical and treatment histories of patients and have the potential to be used to provide better healthcare. A significant portion of the EHR is in the form of clinical notes and Natural Language Processing (NLP) methods can help extract hidden information from them. However, applying NLP in healthcare has challenges. Many of the clinical note datasets are scarce and imbalanced, making it difficult to develop generalizable and robust NLP methods. Additionally, effective use of NLP in healthcare requires close collaboration with medical experts to identify and understand meaningful clinical problems. This dissertation addresses these challenges and explores the application of NLP in healthcare. In Chapter 3 and 4, we develop generalizable and robust NLP methods for clinical note classification and female suicide report coding. In Chapter 5 and 6, we apply NLP to extract symptoms from clinical notes and study risk factors associated with out-of-hospital cardiac arrest (OHCA) and Long COVID.
■590 ▼aSchool code: 0250.
■650 4▼aInformation science
■650 4▼aComputer science
■650 4▼aMedicine
■650 4▼aBioinformatics
■653 ▼aElectronic Health Records
■653 ▼aNatural Language Processing
■653 ▼aOut-of-hospital cardiac arrest
■653 ▼aClinical notes
■690 ▼a0723
■690 ▼a0984
■690 ▼a0564
■690 ▼a0715
■71020▼aUniversity of Washington▼bBiomedical Informatics and Medical Education.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163080▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
