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dc.contributor.authorFarooq, Kamranen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.authorAtassi, Hichamen_UK
dc.contributor.authorLeslie, Stephenen_UK
dc.contributor.authorEckl, Chrisen_UK
dc.contributor.authorMacRae, Calumen_UK
dc.contributor.authorSlack, Warneren_UK
dc.contributor.editorLiu, Den_UK
dc.contributor.editorAlippi, Cen_UK
dc.contributor.editorZhao, Den_UK
dc.contributor.editorHussain, Aen_UK
dc.description.abstractRapid access chest pain clinics (RACPC) enable clinical risk assessment, investigation and arrangement of a treatment plan for chest pain patients without a long waiting list. RACPC Clinicians often experience difficulties in the diagnosis of chest pain due to the inherent complexity of the clinical process and lack of comprehensive automated diagnostic tools. To date, various risk assessment models have been proposed, inspired by the National Institute of Clinical Excellence (NICE) guidelines to provide clinical decision support mechanism in chest pain diagnosis. The aim of this study is to help improve the performance of RACPC, specifically from the clinical decision support perspective. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating 14 risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. In the first phase, a novel binary classification model was developed using a Decision Tree algorithm in conjunction with forward and backward selection wrapping techniques. Secondly, a logistic regression model was trained using all of the given variables combined with forward and backward feature selection techniques to identify the most significant features. The new models have resulted in very good predictive power, demonstrating general performance improvement compared to a state-of-the-art prediction model.en_UK
dc.relationFarooq K, Hussain A, Atassi H, Leslie S, Eckl C, MacRae C & Slack W (2013) A novel clinical expert system for chest pain risk assessment. In: Liu D, Alippi C, Zhao D & Hussain A (eds.) Advances in Brain Inspired Cognitive Systems: 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings. Lecture Notes in Computer Science, 7888. 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, Beijing, China, 09.06.2012-11.06.2013. Berlin Heidelberg: Springer, pp. 296-307.;
dc.relation.ispartofseriesLecture Notes in Computer Science, 7888en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectRACPC risk assessmenten_UK
dc.subjectChest pain decision support systemen_UK
dc.subjectClinical decision support system for chest pain based on NICE Guidelinesen_UK
dc.titleA novel clinical expert system for chest pain risk assessmenten_UK
dc.typeConference Paperen_UK
dc.rights.embargoreason[A novel clinical expert system for chest pain risk assessment.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.btitleAdvances in Brain Inspired Cognitive Systems: 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedingsen_UK
dc.citation.conferencedates2012-06-09 - 2013-06-11en_UK
dc.citation.conferencelocationBeijing, Chinaen_UK
dc.citation.conferencename6th International Conference on Brain Inspired Cognitive Systems, BICS 2013en_UK
dc.publisher.addressBerlin Heidelbergen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationNHS Highlanden_UK
dc.contributor.affiliationSitekit Solutions Ltden_UK
dc.contributor.affiliationBrigham and Women's Hospitalen_UK
dc.contributor.affiliationHarvard Universityen_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
local.rioxx.authorFarooq, Kamran|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.authorAtassi, Hicham|en_UK
local.rioxx.authorLeslie, Stephen|en_UK
local.rioxx.authorEckl, Chris|en_UK
local.rioxx.authorMacRae, Calum|en_UK
local.rioxx.authorSlack, Warner|en_UK
local.rioxx.projectInternal Project|University of Stirling|
local.rioxx.contributorLiu, D|en_UK
local.rioxx.contributorAlippi, C|en_UK
local.rioxx.contributorZhao, D|en_UK
local.rioxx.contributorHussain, A|en_UK
local.rioxx.filenameA novel clinical expert system for chest pain risk assessment.pdfen_UK
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