Improved characterisation of clinical text through ontology-based vocabulary expansion

Journal of Biomedical Semantics

Robert Hoehndorf¹  Luke T. Slater²  Simon Ball²  William Bradlow²  Georgios V Gkoutos³ 

[1] Computational Bioscience Research Centre, KAUST, Thuwal, Saudi Arabia;Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, B15 2TT, Birmingham, UK;University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, B15 2TT, Birmingham, UK;Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, B15 2TT, Birmingham, UK;University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, B15 2TT, Birmingham, UK;NIHR Experimental Cancer Medicine Centre, University of Birmingham, B15 2TT, Birmingham, UK;NIHR Surgical Reconstruction and Microbiology Research Centre, University of Birmingham, B15 2TT, Birmingham, UK;NIHR Biomedical Research Centre, University of Birmingham, B15 2TT, Birmingham, UK;MRC Health Data Research (HDR), Birmingham, UK;

关键词: Text mining;    Ontology;    Vocabulary expansion;    Semantic similarity;   

DOI  :  10.1186/s13326-021-00241-5

来源: Springer

【 摘 要 】

BackgroundBiomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the same entities being described by several concepts in the same or similar contexts across several ontologies. While these concepts describe the same entities, they contain different sets of complementary metadata. Linking these definitions to make use of their combined metadata could lead to improved performance in ontology-based information retrieval, extraction, and analysis tasks.ResultsWe develop and present an algorithm that expands the set of labels associated with an ontology class using a combination of strict lexical matching and cross-ontology reasoner-enabled equivalency queries. Across all disease terms in the Disease Ontology, the approach found 51,362 additional labels, more than tripling the number defined by the ontology itself. Manual validation by a clinical expert on a random sampling of expanded synonyms over the Human Phenotype Ontology yielded a precision of 0.912. Furthermore, we found that annotating patient visits in MIMIC-III with an extended set of Disease Ontology labels led to semantic similarity score derived from those labels being a significantly better predictor of matching first diagnosis, with a mean average precision of 0.88 for the unexpanded set of annotations, and 0.913 for the expanded set.ConclusionsInter-ontology synonym expansion can lead to a vast increase in the scale of vocabulary available for text mining applications. While the accuracy of the extended vocabulary is not perfect, it nevertheless led to a significantly improved ontology-based characterisation of patients from text in one setting. Furthermore, where run-on error is not acceptable, the technique can be used to provide candidate synonyms which can be checked by a domain expert.

【 授权许可】

CC BY