Ontology-based categorization of clinical studies by their conditions

• Published in: Journal of biomedical informatics Vol. 135; p. 104235
• Main Authors: Liu, Hao, Carini, Simona, Chen, Zhehuan, Phillips Hey, Spencer, Sim, Ida, Weng, Chunhua
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2022
• Subjects: Categorization; Clinical Study; Data Visualization; Medical Subject Headings; Ontology; SNOMED CT; Systematized Nomenclature of Medicine; SNOMED CT; Data Visualization; Ontology; Clinical Study; Categorization
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2022.104235

[Display omitted] The free-text Condition data field in the ClinicalTrials.gov is not amenable to computational processes for retrieving, aggregating and visualizing clinical studies by condition categories. This paper contributes a method for automated ontology-based categorization of clinical studies by their conditions. Our method first maps text entries in ClinicalTrials.gov’s Condition field to standard condition concepts in the OMOP Common Data Model by using SNOMED CT as a reference ontology and using Usagi for concept normalization, followed by hierarchical traversal of the SNOMED ontology for concept expansion, ontology-driven condition categorization, and visualization. We compared the accuracy of this method to that of the MeSH-based method. We reviewed the 4,506 studies on Vivli.org categorized by our method. Condition terms of 4,501 (99.89%) studies were successfully mapped to SNOMED CT concepts, and with a minimum concept mapping score threshold, 4,428 (98.27%) studies were categorized into 31 predefined categories. When validating with manual categorization results on a random sample of 300 studies, our method achieved an estimated categorization accuracy of 95.7%, while the MeSH-based method had an accuracy of 85.0%. We showed that categorizing clinical studies using their Condition terms with referencing to SNOMED CT achieved a better accuracy and coverage than using MeSH terms. The proposed ontology-driven condition categorization was useful to create accurate clinical study categorization that enables clinical researchers to aggregate evidence from a large number of clinical studies.