NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov.
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.
Keywords
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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