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dc.contributor.authorPauran, Nargis
dc.date.accessioned2016-06-23T12:30:44Z
dc.date.available2016-06-23T12:30:44Z
dc.date.issued2016-03-12
dc.date.submitted2016-06-15T15:37:42.707Z
dc.identifier.citationPauran, N. 2016: Bayesian Networks for Health Care Support. Queen Mary University of London.en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/13045
dc.descriptionPhDen_US
dc.description.abstractBayesian Networks (BNs) have been considered as a potentially useful technique in the health service domain since they were invented. Many authors have presented BNs for managing health care and waiting time, predicting outcomes, improving treatment recommendation process and many more. Despite all these development effort, BNs have been rarely applied to provide support in any of these clinical areas. This thesis investigates the use of BNs for analysing clinical evidence data from observational studies, currently considered the type of study proving the weakest evidence. It begins by investigating challenges around the analysis of data and evidence faced by health professionals in health service. It then discusses the importance of observational studies to understand how disease, treatments and other clinical factors interact with each other. Further it describes the various techniques, such as using statistical inference methods and clinical judgements, available to justify any discovered interactions. In contrary to Frequentist approaches, Bayesian Networks can combine knowledge and data to derive evidence of relationships between different factors. This thesis proposes a novel way to combine knowledge and observational data in Bayesian Networks to derive evidence for clinical queries. Firstly, it shows how to construct and refine a Bayesian Network model by performing hypothesis tests to check which out of a number of experts’ judged causal relations between a set of domain variables are plausible for the available observational data. Secondly, it proposes techniques to evaluate the strength of all plausible relations/associations. Finally, it shows how these techniques are combined into a novel data analysis method for answering clinical queries by combining knowledge with data. In order to illustrate this method this thesis uses a case study and data about the operation of a multidisciplinary team (MDT) that provided treatment recommendations to cancer patients, at Barts and the London HPB Centre over five years. In summary, the case study shows the potential for the method and allows us to propose ways to present results in a comprehensible format.
dc.description.sponsorshipImpactQMen_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectElectronic Engineering and Computer Scienceen_US
dc.subjectBayesian Networksen_US
dc.subjectclinical evidence dataen_US
dc.titleBayesian Networks for Health Care Support.en_US
dc.typeThesisen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author


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    Theses Awarded by Queen Mary University of London

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