Show simple item record

dc.contributor.authorSlater, Luke T
dc.contributor.authorHoehndorf, Robert
dc.contributor.authorKarwath, Andreas
dc.contributor.authorGkoutos, Georgios
dc.date.accessioned2020-12-06T11:16:57Z
dc.date.available2020-12-06T11:16:57Z
dc.date.issued2020-12-05
dc.identifier.citationSlater, L. T., Hoehndorf, R., Karwath, A., & Gkoutos, G. V. (2020). Exploring binary relations for ontology extension and improved adaptation to clinical text. doi:10.1101/2020.12.04.411751
dc.identifier.doi10.1101/2020.12.04.411751
dc.identifier.urihttp://hdl.handle.net/10754/666282
dc.description.abstractBackground: The controlled domain vocabularies provided by ontologies make them an indispensable tool for text mining. Ontologies also include semantic features in the form of taxonomy and axioms, which make annotated entities in text corpora useful for semantic analysis. Extending those semantic features may improve performance for characterisation and analytic tasks. Ontology learning techniques have previously been explored for novel ontology construction from text, though most recent approaches have focused on literature, with applications in information retrieval or human interaction tasks. We hypothesise that extension of existing ontologies using information mined from clinical narrative text may help to adapt those ontologies such that they better characterise those texts, and lead to improved classification performance. Results: We develop and present a framework for identifying new classes in text corpora, which can be integrated into existing ontology hierarchies. To do this, we employ the Stanford Open Information Extraction algorithm and integrate its implementation into the Komenti semantic text mining framework. To identify whether our approach leads to better characterisation of text, we present a case study, using the method to learn an adaptation to the Disease Ontology using text associated with a sample of 1,000 patient visits from the MIMIC-III critical care database. We use the adapted ontology to annotate and classify shared first diagnosis on patient visits with semantic similarity, revealing an improved performance over use of the base Disease Ontology on the set of visits the ontology was constructed from. Moreover, we show that the adapted ontology also improved performance for the same task over two additional unseen samples of 1,000 and 2,500 patient visits. Conclusions: We report a promising new method for ontology learning and extension from text. We demonstrate that we can successfully use the method to adapt an existing ontology to a textual dataset, improving its ability to characterise the dataset, and leading to improved analytic performance, even on unseen portions of the dataset.
dc.description.sponsorshipGVG and LTS acknowledge support from support from the NIHR Birmingham ECMC, the NIHR 274 Birmingham SRMRC, Nanocommons H2020-EU (731032), OpenRisknet H2020-EINFRA (731075) 275 and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK 276 (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and 277 Social Care (England) and the devolved administrations, and leading medical research charities. The 278 views expressed in this publication are those of the authors and not necessarily those of the NHS, 279 the National Institute for Health Research, the Medical Research Council or the Department of 280 Health. RH and GVG were supported by funding from the King Abdullah University of Science and 281 Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3790-01-01. 282 AK was supported by by the Medical Research Council (MR/S003991/1) and the MRC HDR UK 283 (HDRUK/CFC/01).
dc.description.sponsorshipWe would like to thank Dr Egon Willighagen, and Dr Paul Schofield for helpful discussions 271 surrounding described investigations.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://biorxiv.org/lookup/doi/10.1101/2020.12.04.411751
dc.rightsArchived with thanks to Cold Spring Harbor Laboratory
dc.titleExploring binary relations for ontology extension and improved adaptation to clinical text
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.eprint.versionPre-print
dc.contributor.institutionInstitute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham.
dc.contributor.institutionInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust.
dc.contributor.institutionMRC Health Data Research UK (HDR UK) Midlands.
dc.contributor.institutionNIHR Experimental Cancer Medicine Centre.
dc.contributor.institutionNIHR Surgical Reconstruction and Microbiology Research Centre.
dc.contributor.institutionNIHR Biomedical Research Centre.
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3790-01-01
refterms.dateFOA2020-12-06T11:17:33Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


Files in this item

Thumbnail
Name:
exploring binary.pdf
Size:
263.6Kb
Format:
PDF
Description:
pre print

This item appears in the following Collection(s)

Show simple item record