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A Translational Informatics Framework for Generating Data-Driven Solutions to Clinical Challenges
A Translational Informatics Framework for Generating Data-Driven Solutions to Clinical Challenges
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105539
- ISBN
- 9798265402424
- DDC
- 579.256
- 저자명
- Giuste, Felipe.
- 서명/저자
- A Translational Informatics Framework for Generating Data-Driven Solutions to Clinical Challenges
- 발행사항
- [Sl] : Georgia Institute of Technology, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 148 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Wang, May D.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2023.
- 초록/해제
- 요약The goal of this work was to develop a framework to generate data-driven clinical insights by solving major translational informatics challenges. The three major informatics challenges are: lack of data standardization, opaque models, and inconsistent model deployment. I aimed to apply the framework to solve these challenges and demonstrate its value using real-world case studies. First, I developed a solution to the lack of healthcare information standardization through the creation of a web-based application to standardize healthcare data at a Shriners Children's hospital site using the Fast Health Interoperability Resources (FHIR) data exchange standard. The developed approach allows for robust adaptation across clinical data sources while maintaining interoperability across clinical sites. The deployed application also identifies patient cohorts for conducting future clinical studies. Next, I improve model interpretability by leveraging explainable AI (XAI) solutions to gain insight into the importance of clinical features in predicting COVID-19 positive patient outcomes. A robust feature ranking approach was also developed to facilitate the clinical deployment of a SMART-on-FHIR application for decision support by minimizing the number of required clinical features to successfully predict patient risk. Finally, I implemented the framework to generate a data-driven decision support tool for rare disease detection. Specifically, I generated synthetic data to improve deep learning model performance and developed a user interface to support expert detection of pediatric heart transplant rejection. Together, these case studies demonstrate the value of the proposed framework in solving major challenges in translational informatics.
- 일반주제명
- Coronaviruses
- 일반주제명
- Neural networks
- 일반주제명
- Support vector machines
- 일반주제명
- COVID-19
- 일반주제명
- Computer science
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798265402424
■035 ▼a(MiAaPQ)AAI32315029
■035 ▼a(MiAaPQ)GeorgiaTech71967
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a579.256
■1001 ▼aGiuste, Felipe.
■24512▼aA Translational Informatics Framework for Generating Data-Driven Solutions to Clinical Challenges
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a148 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Wang, May D.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2023.
■520 ▼aThe goal of this work was to develop a framework to generate data-driven clinical insights by solving major translational informatics challenges. The three major informatics challenges are: lack of data standardization, opaque models, and inconsistent model deployment. I aimed to apply the framework to solve these challenges and demonstrate its value using real-world case studies. First, I developed a solution to the lack of healthcare information standardization through the creation of a web-based application to standardize healthcare data at a Shriners Children's hospital site using the Fast Health Interoperability Resources (FHIR) data exchange standard. The developed approach allows for robust adaptation across clinical data sources while maintaining interoperability across clinical sites. The deployed application also identifies patient cohorts for conducting future clinical studies. Next, I improve model interpretability by leveraging explainable AI (XAI) solutions to gain insight into the importance of clinical features in predicting COVID-19 positive patient outcomes. A robust feature ranking approach was also developed to facilitate the clinical deployment of a SMART-on-FHIR application for decision support by minimizing the number of required clinical features to successfully predict patient risk. Finally, I implemented the framework to generate a data-driven decision support tool for rare disease detection. Specifically, I generated synthetic data to improve deep learning model performance and developed a user interface to support expert detection of pediatric heart transplant rejection. Together, these case studies demonstrate the value of the proposed framework in solving major challenges in translational informatics.
■590 ▼aSchool code: 0078.
■650 4▼aCoronaviruses
■650 4▼aNeural networks
■650 4▼aSupport vector machines
■650 4▼aCOVID-19
■650 4▼aComputer science
■690 ▼a0800
■690 ▼a0984
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360513▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
