A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials.

Autor:	Urbanowicz RJ; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States., Holmes JH; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States., Appleby D; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States., Narasimhan V; Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States., Durborow S; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States., Al-Naamani N; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States., Fernando M; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States., Kawut SM; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States.
Jazyk:	angličtina
Zdroj:	Methods of information in medicine [Methods Inf Med] 2022 May; Vol. 61 (1-02), pp. 3-10. Date of Electronic Publication: 2021 Nov 24.
DOI:	10.1055/s-0041-1739361
Abstrakt:	Objective: Data harmonization is essential to integrate individual participant data from multiple sites, time periods, and trials for meta-analysis. The process of mapping terms and phrases to an ontology is complicated by typographic errors, abbreviations, truncation, and plurality. We sought to harmonize medical history (MH) and adverse events (AE) term records across 21 randomized clinical trials in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. Methods: We developed and applied a semi-automated harmonization pipeline for use with domain-expert annotators to resolve ambiguous term mappings using exact and fuzzy matching. We summarized MH and AE term mapping success, including map quality measures, and imputation of a generalizing term hierarchy as defined by the applied Medical Dictionary for Regulatory Activities (MedDRA) ontology standard. Results: Over 99.6% of both MH ( N  = 37,105) and AE ( N  = 58,170) records were successfully mapped to MedDRA low-level terms. Automated exact matching accounted for 74.9% of MH and 85.5% of AE mappings. Term recommendations from fuzzy matching in the pipeline facilitated annotator mapping of the remaining 24.9% of MH and 13.8% of AE records. Imputation of the generalized MedDRA term hierarchy was unambiguous in 85.2% of high-level terms, 99.4% of high-level group terms, and 99.5% of system organ class in MH, and 75% of high-level terms, 98.3% of high-level group terms, and 98.4% of system organ class in AE. Conclusion: This pipeline dramatically reduced the burden of manual annotation for MH and AE term harmonization and could be adapted to other data integration efforts. Competing Interests: None declared. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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