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dc.contributor.authorOgunsanya, Oluwole Victor
dc.date.accessioned2015-09-15T11:44:21Z
dc.date.available2015-09-15T11:44:21Z
dc.date.issued2012-04
dc.identifier.citationOgunsanya, O.V. 2012. Decision Support using Bayesian Networks for Clinical Decision Making. Queen Mary University of London.en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/8688
dc.descriptionPhDen_US
dc.description.abstractThis thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using appropriate statistical distributions. Many practical applications of BNs use the relative frequency approach while translating existing medical knowledge to a prior distribution in a BN model. This approach does not capture the full uncertainty surrounding the prior knowledge. Secondly, it demonstrates a novel use of the multinomial BN formulation in learning parameters of categorical variables. The traditional approach requires fixed number of parameters during the learning process but this framework allows an analyst to generate a multinomial BN model based on the number of parameters required. Thirdly, it presents a novel application of the multinomial BN formulation and dynamic discretization to learning causal relations between variables. The idea is to consider competing causal relations between variables as hypotheses and use data to identify the best hypothesis. The result shows that BN models can provide an alternative to the conventional causal learning techniques. The fourth novel application is the use of Hierarchical Bayesian Network (HBN) models, augmented by dynamic discretization technique, to meta-analysis of clinical data. The result shows that BN models can provide an alternative to classical meta analysis techniques. The thesis presents two clinical case studies to demonstrate these novel applications of BN models. The first case study uses data from a multi-disciplinary team at the Royal London hospital to demonstrate the flexibility of the multinomial BN framework in learning parameters of a clinical model. The second case study demonstrates the use of BN and dynamic discretization to solving decision problem. In summary, the combination of the Junction Tree Algorithm and Dynamic Discretization Algorithm provide a unified modeling framework for solving interesting clinical problems.en_US
dc.description.sponsorshipEPSRC; Agena Ltden_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectComputer Scienceen_US
dc.subjectBayesian Networksen_US
dc.subjectDynamic Discretization Algorithmen_US
dc.titleDecision Support using Bayesian Networks for Clinical Decision Making.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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