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dc.contributor.authorArguello-Casteleiro, Mercedesen_UK
dc.contributor.authorHenson, Cen_UK
dc.contributor.authorMaroto, Navaen_UK
dc.contributor.authorLi, Saihongen_UK
dc.contributor.authorDes-Diz, Julioen_UK
dc.contributor.authorFernandez-Prieto, Maria Jesusen_UK
dc.contributor.authorPeters, Sarahen_UK
dc.contributor.authorFurmston, Timen_UK
dc.contributor.authorSevillano Torrado, Carlosen_UK
dc.contributor.authorMaseda Fernandez, Diegoen_UK
dc.contributor.authorKulshrestha, Men_UK
dc.contributor.authorKeane, Johnen_UK
dc.contributor.authorStevens, Roberten_UK
dc.contributor.authorWroe, Chrisen_UK
dc.description.abstractEmergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits.en_UK
dc.relationArguello-Casteleiro M, Henson C, Maroto N, Li S, Des-Diz J, Fernandez-Prieto MJ, Peters S, Furmston T, Sevillano Torrado C, Maseda Fernandez D, Kulshrestha M, Keane J, Stevens R & Wroe C (2022) MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19. In: SWAT4HCLS 2022: Semantic Web Applications and Tools for Health Care and Life Sciences. CEUR Workshop Proceedings, 3127. 13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, Leiden, Netherlands, 10.01.2022-14.01.2022. Leiden: CEUR-WS, pp. 108-117.
dc.relation.ispartofseriesCEUR Workshop Proceedings, 3127en_UK
dc.rightsCopyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0 -
dc.subjectDeep Learning for Natural Language Processingen_UK
dc.subjectstatic embeddingsen_UK
dc.subjecttransformer-based language modelsen_UK
dc.titleMetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19en_UK
dc.typeConference Paperen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.btitleSWAT4HCLS 2022: Semantic Web Applications and Tools for Health Care and Life Sciencesen_UK
dc.citation.conferencedates2022-01-10 - 2022-01-14en_UK
dc.citation.conferencelocationLeiden, Netherlandsen_UK
dc.citation.conferencename13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciencesen_UK
dc.contributor.affiliationUniversity of Manchesteren_UK
dc.contributor.affiliationMid Cheshire Hospitals NHS Foundation Trusten_UK
dc.contributor.affiliationUniversidad Politécnica de Madriden_UK
dc.contributor.affiliationServizo Galego de Saudeen_UK
dc.contributor.affiliationUniversity of Salforden_UK
dc.contributor.affiliationUniversity of Manchesteren_UK
dc.contributor.affiliationUniversity of Manchesteren_UK
dc.contributor.affiliationServizo Galego de Saudeen_UK
dc.contributor.affiliationServizo Galego de Saudeen_UK
dc.contributor.affiliationMid Cheshire Hospitals NHS Foundation Trusten_UK
dc.contributor.affiliationUniversity of Manchesteren_UK
dc.contributor.affiliationUniversity of Manchesteren_UK
dc.contributor.affiliationBMJ Publishing Groupen_UK
rioxxterms.apcnot chargeden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
local.rioxx.authorArguello-Casteleiro, Mercedes|en_UK
local.rioxx.authorHenson, C|en_UK
local.rioxx.authorMaroto, Nava|en_UK
local.rioxx.authorLi, Saihong|0000-0003-2503-607Xen_UK
local.rioxx.authorDes-Diz, Julio|en_UK
local.rioxx.authorFernandez-Prieto, Maria Jesus|en_UK
local.rioxx.authorPeters, Sarah|en_UK
local.rioxx.authorFurmston, Tim|en_UK
local.rioxx.authorSevillano Torrado, Carlos|en_UK
local.rioxx.authorMaseda Fernandez, Diego|en_UK
local.rioxx.authorKulshrestha, M|en_UK
local.rioxx.authorKeane, John|en_UK
local.rioxx.authorStevens, Robert|en_UK
local.rioxx.authorWroe, Chris|en_UK
local.rioxx.projectInternal Project|University of Stirling|
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