{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T04:03:06Z","timestamp":1754020986508},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/shti220152","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:33:05Z","timestamp":1654594385000},"source":"Crossref","is-referenced-by-count":2,"title":["NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov"],"prefix":"10.3233","author":[{"given":"Fatemeh","family":"Shah-Mohammadi","sequence":"first","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irena","family":"Parvanova","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220152","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:33:06Z","timestamp":1654594386000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220152","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}