dc.contributor.author | Ogunsanya, Oluwole Victor | |
dc.date.accessioned | 2015-09-15T11:44:21Z | |
dc.date.available | 2015-09-15T11:44:21Z | |
dc.date.issued | 2012-04 | |
dc.identifier.citation | Ogunsanya, O.V. 2012. Decision Support using Bayesian Networks for Clinical Decision Making. Queen Mary University of London. | en_US |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688 | |
dc.description | PhD | en_US |
dc.description.abstract | This 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.sponsorship | EPSRC; Agena Ltd | en_US |
dc.language.iso | en | en_US |
dc.publisher | Queen Mary University of London | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Bayesian Networks | en_US |
dc.subject | Dynamic Discretization Algorithm | en_US |
dc.title | Decision Support using Bayesian Networks for Clinical Decision Making. | en_US |
dc.type | Thesis | en_US |
dc.rights.holder | The 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 | |