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    Komenti: A semantic text mining framework

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    Type
    Preprint
    Authors
    Slater, Luke T cc
    Bradlow, William
    Hoehndorf, Robert cc
    Motti, Dino FA
    Ball, Simon
    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
    2020-08-05
    Permanent link to this record
    http://hdl.handle.net/10754/664658
    
    Metadata
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    Abstract
    Komenti is a reasoner-enabled semantic query and information extraction tool. It is the only text mining tool that enables querying inferred knowledge from biomedical ontologies. It also contains multiple novel components for vocabulary construction and context disambiguation, which can improve the power of text mining and ontology-based analysis tasks, with a view towards making full use of the semantic provision of biomedical ontologies in the text extraction and characterisation space. Here, we describe Komenti, its features, and a use case wherein we automate a clinical audit process, classifying the medications of patients with hypertrophic cardiomyopathy from text records, revealing a high precision, and a subcohort of candidate patients who have atrial fibrillation but were not anti-coagulated, and are therefore at a higher risk of stroke.
    Citation
    Slater, L. T., Bradlow, W., Hoehndorf, R., Motti, D. F., Ball, S., & Gkoutos, G. V. (2020). Komenti: A semantic text mining framework. doi:10.1101/2020.08.04.233049
    Sponsors
    The UoB Ethical Review approved this work (ERN 20-0338). GVG and LTS acknowledge support 95 from support from the NIHR Birmingham ECMC, the NIHR Birmingham SRMRC, Nanocommons 96 H2020-EU (731032),and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK 97(HDRUK/CFC/01).
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.08.04.233049
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2020.08.04.233049
    ae974a485f413a2113503eed53cd6c53
    10.1101/2020.08.04.233049
    Scopus Count
    Collections
    Bio-Ontology Research Group (BORG); Preprints; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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