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User Centered Rare Disease Clinical Trial Knowledge Graph (RCTKG).
Yang, Jeremy Parker; Leadman, Devon; Ballew, Richard M; Sid, Eric; Xu, Yanji; Mathé, Ewy A; Zhu, Qian.
Afiliação
  • Yang JP; University of Wisconsin-Madison, Madison, WI, US.
  • Leadman D; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, US.
  • Ballew RM; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, US.
  • Sid E; ICF International Inc, Rockville, MD, US.
  • Xu Y; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, US.
  • Mathé EA; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, US.
  • Zhu Q; Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, US.
Stud Health Technol Inform ; 310: 94-98, 2024 Jan 25.
Article em En | MEDLINE | ID: mdl-38269772
ABSTRACT
Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reconhecimento Automatizado de Padrão / Doenças Raras Limite: Humans Idioma: En Revista: Stud Health Technol Inform Assunto da revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reconhecimento Automatizado de Padrão / Doenças Raras Limite: Humans Idioma: En Revista: Stud Health Technol Inform Assunto da revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos