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Data-Driven Modeling of Pathological Mechanisms of Dyspnea in Heart Failure
Data-Driven Modeling of Pathological Mechanisms of Dyspnea in Heart Failure
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103021
- ISBN
- 9798314832653
- DDC
- 610.73
- 서명/저자
- Data-Driven Modeling of Pathological Mechanisms of Dyspnea in Heart Failure
- 발행사항
- [Sl] : University of Pittsburgh, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 159 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Henker, Richard.
- 학위논문주기
- Thesis (Ph.D.)--University of Pittsburgh, 2025.
- 초록/해제
- 요약Dyspnea, or difficulty breathing, is one of the leading reasons for seeking emergency care among the six million Americans struggling to live with chronic heart failure in the US. Current clinical risk scores used in the emergency departments are neither sensitive nor specific in triaging dyspnea in these patients, which could delay life-saving therapeutics from those in utmost need or lead to unnecessary admissions and excessive diagnostic testing in those with benign underlying etiologies of dyspnea. This project aims to develop a data-driven clinical decision support tool to triage and phenotype the underlying etiology of dyspnea as well as risk-stratify patients with a known history of heart failure seeking emergency care, potentially improving patient outcomes, and reducing associated healthcare costs. The three manuscripts shaping the deliverables for this project reflect two of three specific aims: 1) scoping review of literature regarding risk stratification and prognostication tools for patients with HF during acute events; 2) manuscript describing the diagnostic decision support tool that denotes the likelihood of the underlying dyspnea source based on clinically meaningful data elements available during initial ED triage; and 3) manuscript describing an intelligent risk stratification tool to triage severity of illness in patients with HF presenting to the ED with dyspnea.
- 일반주제명
- Nursing
- 일반주제명
- Medicine
- 일반주제명
- Health sciences
- 일반주제명
- Pathology
- 키워드
- Data science
- 키워드
- Dyspnea
- 키워드
- Heart failure
- 기타저자
- University of Pittsburgh Nursing
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798314832653
■035 ▼a(MiAaPQ)AAI31844527
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610.73
■1001 ▼aKraevsky-Phillips, Karina.▼0(orcid)0000-0003-2362-7749
■24510▼aData-Driven Modeling of Pathological Mechanisms of Dyspnea in Heart Failure
■260 ▼a[Sl]▼bUniversity of Pittsburgh▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a159 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Henker, Richard.
■5021 ▼aThesis (Ph.D.)--University of Pittsburgh, 2025.
■520 ▼aDyspnea, or difficulty breathing, is one of the leading reasons for seeking emergency care among the six million Americans struggling to live with chronic heart failure in the US. Current clinical risk scores used in the emergency departments are neither sensitive nor specific in triaging dyspnea in these patients, which could delay life-saving therapeutics from those in utmost need or lead to unnecessary admissions and excessive diagnostic testing in those with benign underlying etiologies of dyspnea. This project aims to develop a data-driven clinical decision support tool to triage and phenotype the underlying etiology of dyspnea as well as risk-stratify patients with a known history of heart failure seeking emergency care, potentially improving patient outcomes, and reducing associated healthcare costs. The three manuscripts shaping the deliverables for this project reflect two of three specific aims: 1) scoping review of literature regarding risk stratification and prognostication tools for patients with HF during acute events; 2) manuscript describing the diagnostic decision support tool that denotes the likelihood of the underlying dyspnea source based on clinically meaningful data elements available during initial ED triage; and 3) manuscript describing an intelligent risk stratification tool to triage severity of illness in patients with HF presenting to the ED with dyspnea.
■590 ▼aSchool code: 0178.
■650 4▼aNursing
■650 4▼aMedicine
■650 4▼aHealth sciences
■650 4▼aPathology
■653 ▼aData science
■653 ▼aDyspnea
■653 ▼aEmergency departments
■653 ▼aHeart failure
■653 ▼aPathological mechanisms
■690 ▼a0569
■690 ▼a0566
■690 ▼a0564
■690 ▼a0769
■690 ▼a0571
■71020▼aUniversity of Pittsburgh▼bNursing.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0178
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356709▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
