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Interactive Similarity-Based Search of Clinical Trials.
Sun, Yingcheng; Tang, Jiaqi; Butler, Alex; Liu, Cong; Fang, Yilu; Weng, Chunhua.
Afiliación
  • Sun Y; Department of Biomedical Informatics, Columbia University, New York, NY, USA.
  • Tang J; Data Science Institute, Columbia University, New York, NY, USA.
  • Butler A; Department of Biomedical Informatics, Columbia University, New York, NY, USA.
  • Liu C; Department of Medicine Columbia University, New York, NY, USA.
  • Fang Y; Department of Biomedical Informatics, Columbia University, New York, NY, USA.
  • Weng C; Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Stud Health Technol Inform ; 290: 309-313, 2022 Jun 06.
Article en En | MEDLINE | ID: mdl-35673024
ABSTRACT
The rapid growth of clinical trials launched in recent years poses significant challenges for accurate and efficient trial search. Keyword-based clinical trial search engines require users to construct effective queries, which can be a difficult task given complex information needs. In this study, we present an interactive clinical trial search interface that retrieves trials similar to a target clinical trial. It enables user configuration of 13 clinical trial features and 4 metrics (Jaccard similarity, semantic-based similarity, temporal overlap and geographical distance) to measure pairwise trial similarities. Among 1,007 coronavirus disease 2019 (COVID-19) trials conducted in the United States, 91.9% were found to have similar trials with the similarity threshold being 0.85 and 43.8% were highly similar with the threshold 0.95. A simulation study using 3 groups of similar trials curated by COVID-19 clinical trial reviews demonstrates the precision and recall of the search interface.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos