Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials.
J Biomed Inform
; 118: 103790, 2021 06.
Article
in English
| MEDLINE | ID: covidwho-1196724
ABSTRACT
Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computer Simulation
/
Clinical Trials as Topic
/
Eligibility Determination
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
J Biomed Inform
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
J.jbi.2021.103790
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