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    Improved characterisation of clinical text through ontology-based vocabulary expansion

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    Type
    Preprint
    Authors
    Slater, Luke T cc
    Bradlow, William
    Ball, Simon
    Hoehndorf, Robert cc
    Gkoutos, Georgios cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-07-11
    Permanent link to this record
    http://hdl.handle.net/10754/664429
    
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    Abstract
    AbstractBackgroundBiomedical 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 concepts being described by several terms in the same or similar contexts across several ontologies. While these terms describe the same concepts, 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.
    Citation
    Slater, L. T., Bradlow, W., Ball, S., Hoehndorf, R., & Gkoutos, G. V. (2020). Improved characterisation of clinical text through ontology-based vocabulary expansion. doi:10.1101/2020.07.10.197541
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.07.10.197541
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2020.07.10.197541
    https://www.biorxiv.org/content/biorxiv/early/2020/07/10/2020.07.10.197541.full.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1101/2020.07.10.197541
    Scopus Count
    Collections
    Bio-Ontology Research Group (BORG); Preprints; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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