Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24023
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dc.contributor.authorFarooq, Kamranen_UK
dc.contributor.authorHussain, Amiren_UK
dc.date.accessioned2016-10-05T22:27:09Z-
dc.date.available2016-10-05T22:27:09Z-
dc.date.issued2016-12en_UK
dc.identifier.other12en_UK
dc.identifier.urihttp://hdl.handle.net/1893/24023-
dc.description.abstractPurpose  This multidisciplinary industrial research project sets out to develop a hybrid clinical decision support mechanism (inspired by ontology and machine learning driven techniques) by combining evidence, extrapolated through legacy patient data to facilitate cardiovascular preventative care.  Methods  The proposed cardiovascular clinical decision support framework comprises of two novel key components: (1) Ontology driven clinical risk assessment and recommendation system (ODCRARS) (2) Machine learning driven prognostic system (MLDPS). State of the art machine learning and feature selection methods are utilised for the prognostic modelling purposes. The ODCRARS is a knowledge-based system which is based on clinical expert’s knowledge, encoded in the form of clinical rules engine to carry out cardiac risk assessment for various cardiovascular diseases. The MLDPS is a non knowledge-based/data driven system which is developed using state of the art machine learning and feature selection techniques applied on real patient datasets. Clinical case studies in the RACPC, heart disease and breast cancer domains are considered for the development and clinical validation purposes. For the purpose of this paper, clinical case study in the RACPC/chest pain domain will be discussed in detail from the development and validation perspective.  Results  The proposed clinical decision support framework is validated through clinical case studies in the cardiovascular domain. This paper demonstrates an effective cardiovascular decision support mechanism for handling inaccuracies in the clinical risk assessment of chest pain patients and help clinicians effectively distinguish acute angina/cardiac chest pain patients from those with other causes of chest pain.  Conclusion  The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. Various chest pain risk assessment prototypes have been developed and deployed online for further clinical trials.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationFarooq K & Hussain A (2016) A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system. Complex Adaptive Systems Modeling, 4, Art. No.: 12. https://doi.org/10.1186/s40294-016-0023-xen_UK
dc.rights© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectClinical decision support frameworken_UK
dc.subjectCardiovascular decision support frameworken_UK
dc.subjectHybrid clinical decision support frameworken_UK
dc.titleA novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical systemen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1186/s40294-016-0023-xen_UK
dc.citation.jtitleComplex Adaptive Systems Modelingen_UK
dc.citation.issn2194-3206en_UK
dc.citation.volume4en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.citation.date12/07/2016en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000380161600001en_UK
dc.identifier.wtid553280en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2016-06-28en_UK
dcterms.dateAccepted2016-06-28en_UK
dc.date.filedepositdate2016-08-12en_UK
rioxxterms.apcpaiden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorFarooq, Kamran|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2016-08-15en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2016-08-15|en_UK
local.rioxx.filenamenovel ontology and machine learning.pdfen_UK
local.rioxx.filecount1en_UK
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