Introducing an ontology-driven pipeline for the identification of common data elements

Anas Elghafari; Joseph Finkelstein

doi:10.3233/SHTI200574

Introducing an ontology-driven pipeline for the identification of common data elements

Anas Elghafari^*, Joseph Finkelstein

^*Corresponding author for this work

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

Abstract

Common Data Elements (CDEs) are necessary for ensuring data sharing across studies, providing comparability, and enabling aggregation and meta-analyses. The process of developing a set of CDEs for a given clinical research area has typically been arduous and time-consuming. In this work we introduce an automated pipeline that can greatly aid the process by identifying, aggregating, and ranking relevant CDEs from the outcomes of studies registered on clinicaltrials.gov (CTG). The pipeline uses the Medical Subject Headings (MeSH) ontology to group and rank candidate CDEs by specific diseases. The initial CDE pipeline has been tested using an emerging research domain. The resulting CDEs output was aligned with the current recommendations in the corresponding subject area. Further development of automated means for CDE generation based on structured information from CTG and MeSH is warranted.

Original language	English
Title of host publication	THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC
Editors	John Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias
Publisher	IOS Press
Pages	379-382
Number of pages	4
ISBN (Electronic)	9781643680927
DOIs	https://doi.org/10.3233/SHTI200574
Publication status	Published - 2020
Externally published	Yes

Publication series

Series	Studies in Health Technology and Informatics
Volume	272
ISSN	0926-9630

Keywords

automated data extraction
clinical trials
clinicaltrials.gov
Common Data Elements
MeSH tree
outcomes
xml

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10.3233/SHTI200574Licence: CC BY-NC

Cite this

Elghafari, Anas ; Finkelstein, Joseph. / Introducing an ontology-driven pipeline for the identification of common data elements. THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. editor / John Mantas ; Arie Hasman ; Mowafa S. Househ ; Parisis Gallos ; Emmanouil Zoulias. IOS Press, 2020. pp. 379-382 (Studies in Health Technology and Informatics, Vol. 272).

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title = "Introducing an ontology-driven pipeline for the identification of common data elements",

abstract = "Common Data Elements (CDEs) are necessary for ensuring data sharing across studies, providing comparability, and enabling aggregation and meta-analyses. The process of developing a set of CDEs for a given clinical research area has typically been arduous and time-consuming. In this work we introduce an automated pipeline that can greatly aid the process by identifying, aggregating, and ranking relevant CDEs from the outcomes of studies registered on clinicaltrials.gov (CTG). The pipeline uses the Medical Subject Headings (MeSH) ontology to group and rank candidate CDEs by specific diseases. The initial CDE pipeline has been tested using an emerging research domain. The resulting CDEs output was aligned with the current recommendations in the corresponding subject area. Further development of automated means for CDE generation based on structured information from CTG and MeSH is warranted.",

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author = "Anas Elghafari and Joseph Finkelstein",

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year = "2020",

doi = "10.3233/SHTI200574",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "379--382",

editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias",

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Elghafari, A & Finkelstein, J 2020, Introducing an ontology-driven pipeline for the identification of common data elements. in J Mantas, A Hasman, MS Househ, P Gallos & E Zoulias (eds), THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. IOS Press, Studies in Health Technology and Informatics, vol. 272, pp. 379-382. https://doi.org/10.3233/SHTI200574

Introducing an ontology-driven pipeline for the identification of common data elements. / Elghafari, Anas; Finkelstein, Joseph.
THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. ed. / John Mantas; Arie Hasman; Mowafa S. Househ; Parisis Gallos; Emmanouil Zoulias. IOS Press, 2020. p. 379-382 (Studies in Health Technology and Informatics, Vol. 272).

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

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AU - Elghafari, Anas

AU - Finkelstein, Joseph

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N2 - Common Data Elements (CDEs) are necessary for ensuring data sharing across studies, providing comparability, and enabling aggregation and meta-analyses. The process of developing a set of CDEs for a given clinical research area has typically been arduous and time-consuming. In this work we introduce an automated pipeline that can greatly aid the process by identifying, aggregating, and ranking relevant CDEs from the outcomes of studies registered on clinicaltrials.gov (CTG). The pipeline uses the Medical Subject Headings (MeSH) ontology to group and rank candidate CDEs by specific diseases. The initial CDE pipeline has been tested using an emerging research domain. The resulting CDEs output was aligned with the current recommendations in the corresponding subject area. Further development of automated means for CDE generation based on structured information from CTG and MeSH is warranted.

AB - Common Data Elements (CDEs) are necessary for ensuring data sharing across studies, providing comparability, and enabling aggregation and meta-analyses. The process of developing a set of CDEs for a given clinical research area has typically been arduous and time-consuming. In this work we introduce an automated pipeline that can greatly aid the process by identifying, aggregating, and ranking relevant CDEs from the outcomes of studies registered on clinicaltrials.gov (CTG). The pipeline uses the Medical Subject Headings (MeSH) ontology to group and rank candidate CDEs by specific diseases. The initial CDE pipeline has been tested using an emerging research domain. The resulting CDEs output was aligned with the current recommendations in the corresponding subject area. Further development of automated means for CDE generation based on structured information from CTG and MeSH is warranted.

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A2 - Hasman, Arie

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A2 - Zoulias, Emmanouil

PB - IOS Press

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Introducing an ontology-driven pipeline for the identification of common data elements

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