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Title: Bayesian networks for health care support
Author: Pauran, Nargis
ISNI:       0000 0004 7962 4448
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2016
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Bayesian 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.
Supervisor: Not available Sponsor: ImpactQM
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Electronic Engineering and Computer Science ; Bayesian Networks ; clinical evidence data