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

Fatemeh Shah-Mohammadi; Irena Parvanova; Joseph Finkelstein

doi:10.3233/SHTI220152

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

Fatemeh Shah-Mohammadi, Irena Parvanova, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	MEDINFO 2021
Subtitle of host publication	One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics
Editors	Paula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Publisher	IOS Press BV
Pages	622-626
Number of pages	5
ISBN (Electronic)	9781643682648
DOIs	https://doi.org/10.3233/SHTI220152
State	Published - 6 Jun 2022
Event	18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 - Virtual, Online Duration: 2 Oct 2021 → 4 Oct 2021

Publication series

Name	Studies in Health Technology and Informatics
Volume	290
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021
City	Virtual, Online
Period	2/10/21 → 4/10/21

Keywords

COVID-19
Core outcome set
Natural language processing (NLP)

Access to Document

10.3233/SHTI220152

Cite this

Shah-Mohammadi, F., Parvanova, I., & Finkelstein, J. (2022). NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov. In P. Otero, P. Scott, S. Z. Martin, & E. Huesing (Eds.), MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics (pp. 622-626). (Studies in Health Technology and Informatics; Vol. 290). IOS Press BV. https://doi.org/10.3233/SHTI220152

Shah-Mohammadi, Fatemeh ; Parvanova, Irena ; Finkelstein, Joseph. / NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov. MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. editor / Paula Otero ; Philip Scott ; Susan Z. Martin ; Elaine Huesing. IOS Press BV, 2022. pp. 622-626 (Studies in Health Technology and Informatics).

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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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Shah-Mohammadi, F, Parvanova, I & Finkelstein, J 2022, NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov. in P Otero, P Scott, SZ Martin & E Huesing (eds), MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 290, IOS Press BV, pp. 622-626, 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021, Virtual, Online, 2/10/21. https://doi.org/10.3233/SHTI220152

NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov. / Shah-Mohammadi, Fatemeh; Parvanova, Irena; Finkelstein, Joseph.
MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. ed. / Paula Otero; Philip Scott; Susan Z. Martin; Elaine Huesing. IOS Press BV, 2022. p. 622-626 (Studies in Health Technology and Informatics; Vol. 290).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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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Shah-Mohammadi F, Parvanova I, Finkelstein J. NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov. In Otero P, Scott P, Martin SZ, Huesing E, editors, MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. IOS Press BV. 2022. p. 622-626. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220152

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

Abstract

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Keywords

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