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dc.contributor.authorWang, A.
dc.contributor.authorMahfouf, M.
dc.contributor.authorMills, G.H.
dc.contributor.authorPanoutsos, G.
dc.contributor.authorLinkens, D.A.
dc.contributor.authorGoode, K.
dc.contributor.authorDenai, Mouloud
dc.identifier.citationWang , A , Mahfouf , M , Mills , G H , Panoutsos , G , Linkens , D A , Goode , K & Denai , M 2010 , ' Intelligent Model-Based Advisory System for the Management of Ventilated Intensive Care Patients – Part I : Hybrid Blood Gas Patient Model ' , Computer Methods and Programs in Biomedicine , vol. 99 , no. 2 , pp. 195-207 .
dc.identifier.otherPURE: 2915465
dc.identifier.otherPURE UUID: 71fb5af4-994c-4cfb-afa6-1cc02221ebcf
dc.identifier.otherScopus: 77954312237
dc.description.abstractArterial blood gas (ABG) analyses are essential for assessing the acid–base status and guiding the adjustment of mechanical ventilation in critically ill patients. Conventional ABG sampling requires repeated arterial punctures or the insertion of an arterial catheter causing pain, haemorrhage and thrombosis to the patients. Less invasive and non-invasive blood gas analysers, with a technology still in transition, have offered some promise in the recent years. SOPAVent (Simulation of Patients under Artificial Ventilation) is a five compartment blood gas model which captures the basic features of respiratory physiology and gas exchange in the human lungs. It uses ventilator settings and routinely monitored physiological parameters as inputs to produce steady-state estimates of the patient's ABG. This paper overviews the original SOPAVent model and presents an improved data-driven hybrid model that is patient-specific and gives continuous and totally non-invasive ABG predictions. The model has been comprehensively tested in simulations and validated using recorded measurements of ABG and ventilator parameters from ICU patientsen
dc.relation.ispartofComputer Methods and Programs in Biomedicine
dc.titleIntelligent Model-Based Advisory System for the Management of Ventilated Intensive Care Patients – Part I : Hybrid Blood Gas Patient Modelen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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