Mapping of UK Biobank clinical codes: Challenges and possible solutions

• Published in: PloS one Vol. 17; no. 12; p. e0275816
• Main Authors: Stroganov, Oleg, Fedarovich, Alena, Wong, Emily, Skovpen, Yulia, Pakhomova, Elena, Grishagin, Ivan, Fedarovich, Dzmitry, Khasanova, Tania, Merberg, David, Szalma, Sándor, Bryant, Julie
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.12.2022; Public Library of Science (PLoS)
• Subjects: Automation; Biobanks; Biological Specimen Banks; Biology and Life Sciences; Cancer; Classification; Codes; Computer and Information Sciences; Data entry; Data integration; Datasets; Dictionaries; Disease; Electronic health records; Evaluation; Hospitals; Humans; Information management; International Classification of Diseases; Jargon; Mapping; Medical laboratories; Medical materials; Medical research; Medicine and Health Sciences; Meningitis; Nomenclature; Ontology; Oral cancer; Primary care; Questionnaires; Skin cancer; Standardization; Systematized Nomenclature of Medicine; United Kingdom; Vocabulary, Controlled; United Kingdom; United Kingdom > UK; England
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0275816

The UK Biobank provides a rich collection of longitudinal clinical data coming from different healthcare providers and sources in England, Wales, and Scotland. Although extremely valuable and available to a wide research community, the heterogeneous dataset contains inconsistent medical terminology that is either aligned to several ontologies within the same category or unprocessed. To make these data useful to a research community, data cleaning, curation, and standardization are needed. Significant efforts to perform data reformatting, mapping to any selected ontologies (such as SNOMED-CT) and harmonization are required from any data user to integrate UK Biobank hospital inpatient and self-reported data, data from various registers with primary care (GP) data. The integrated clinical data would provide a more comprehensive picture of one's medical history. We evaluated several approaches to map GP clinical Read codes to International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies. The results were compared, mapping inconsistencies were flagged, a quality category was assigned to each mapping to evaluate overall mapping quality. We propose a curation and data integration pipeline for harmonizing diagnosis. We also report challenges identified in mapping Read codes from UK Biobank GP tables to ICD and SNOMED CT. Some of the challenges-the lack of precise one-to-one mapping between ontologies or the need for additional ontology to fully map terms-are general reflecting trade-offs to be made at different steps. Other challenges are due to automatic mapping and can be overcome by leveraging existing mappings, supplemented with automated and manual curation.