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Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAWUKLU, Kokou Yvon
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMOUGIN, Fleur
IDREF: 116242337
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGRIFFIER, Romain
dc.contributor.authorBIENVENU, Meghyn
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJOUHET, Vianney
dc.date.accessioned2025-04-22T07:48:45Z
dc.date.available2025-04-22T07:48:45Z
dc.date.issued2025-03-21
dc.identifier.issn1532-0464en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/206343
dc.description.abstractEnOBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers. MATERIALS AND METHODS: We crafted an ontology to encapsulate crucial domain knowledge, leveraging it to query the Clinical Data Warehouse (CDW) of Bordeaux University Hospital. Our work aimed at accurately representing domain knowledge and identifying inconsistencies through ontological axioms. Specifically, our aim was to pinpoint lung cancer patients with EGFR or ALK mutations treated with tyrosine kinase inhibitors (TKIs). We evaluated the ability of this ontology to retrieve and characterize patients in comparison with a traditional SQL queries executed on the CDW. RESULTS: The ontology's results closely aligned with those of the SQL queries. A sub-cohort of 60 lung cancer patients with conflicting information was identified, highlighting inconsistencies in the data. Moreover, the ontology complemented the existing data, uncovering additional information and enriching the dataset. DISCUSSION: This work has highlighted challenges in managing temporal data and handling imperfect data. Addressing these challenges is essential for the effective use of CDW in phenotyping. CONCLUSION: Ontologies improve data quality by identifying inconsistencies, enhancing data completeness, facilitating complex SQL queries, and standardize processes. Developing a framework to manage inconsistent healthcare data, considering its temporal nature, is essential.
dc.language.isoENen_US
dc.subject.enData quality
dc.subject.enEHR
dc.subject.enLung cancer
dc.subject.enOMQA
dc.subject.enPhenotyping
dc.title.enOntology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping
dc.title.alternativeJ Biomed Informen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.jbi.2025.104808en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed40122479en_US
bordeaux.journalJournal of Biomedical Informaticsen_US
bordeaux.page104808en_US
bordeaux.volume165en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-05041944
hal.version1
hal.date.transferred2025-04-22T07:48:48Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Biomedical%20Informatics&rft.date=2025-03-21&rft.volume=165&rft.spage=104808&rft.epage=104808&rft.eissn=1532-0464&rft.issn=1532-0464&rft.au=AWUKLU,%20Kokou%20Yvon&MOUGIN,%20Fleur&GRIFFIER,%20Romain&BIENVENU,%20Meghyn&JOUHET,%20Vianney&rft.genre=article


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