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NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov.
Shah-Mohammadi, Fatemeh; Parvanova, Irena; Finkelstein, Joseph.
  • Shah-Mohammadi F; Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Parvanova I; Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Finkelstein J; Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Stud Health Technol Inform ; 290: 622-626, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1933569
ABSTRACT
Core outcome sets (COS) are necessary to ensure the systematic collection, metadata analysis and sharing the information across studies. However, development of an area-specific clinical research is costly and time consuming. ClinicalTrials.gov, as a public repository, provides access to a vast collection of clinical trials and their characteristics such as primary outcomes. With the growing number of COVID-19 clinical trials, identifying COSs from outcomes of such trials is crucial. This paper introduces a semi-automatic pipeline that can efficiently identify, aggregate and rank the COS from the primary outcomes of COVID-19 clinical trials. Using Natural language processing (NLP) techniques, our proposed pipeline successfully downloads and processes 5090 trials from all over the world and identifies COVID-19-specific outcomes that appeared in more than 1% of the trials. The top-of-the-list outcomes identified by the pipeline are mortality due to COVID-19, COVID-19 infection rate and COVID-19 symptoms.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Natural Language Processing / COVID-19 Type of study: Prognostic study / Randomized controlled trials / Systematic review/Meta Analysis Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220152

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Natural Language Processing / COVID-19 Type of study: Prognostic study / Randomized controlled trials / Systematic review/Meta Analysis Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220152