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dc.contributor.authorSlater, Luke T
dc.contributor.authorBradlow, William
dc.contributor.authorHoehndorf, Robert
dc.contributor.authorMotti, Dino FA
dc.contributor.authorBall, Simon
dc.contributor.authorGkoutos, Georgios
dc.date.accessioned2020-08-18T13:21:34Z
dc.date.available2020-08-18T13:21:34Z
dc.date.issued2020-08-05
dc.identifier.citationSlater, 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
dc.identifier.doi10.1101/2020.08.04.233049
dc.identifier.urihttp://hdl.handle.net/10754/664658
dc.description.abstractKomenti 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.
dc.description.sponsorshipThe 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).
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://biorxiv.org/lookup/doi/10.1101/2020.08.04.233049
dc.rightsArchived with thanks to Cold Spring Harbor Laboratory
dc.titleKomenti: A semantic text mining framework
dc.typePreprint
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionInstitute of Cancer and Genomic Sciences, University of Birmingham, UK.
dc.contributor.institutionUniversity Hospitals Birmingham, NHS Foundation Trust, United Kingdom.
dc.contributor.institutionMRC Health Data Research UK (HDR UK) Midlands, UK.
kaust.personHoehndorf, Robert
refterms.dateFOA2020-08-18T13:22:30Z


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