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Multiply sectioned Bayesian belief networks for large knowledge-based systems: an application to neuromuscular diagnosis

University of British Columbia

In medical diagnosis a proper uncertainty calculus is crucial in knowledge representation. Finite calculus is close to human language and should facilitate knowledge acquisition. An investigation into the feasibility of finite totally ordered probability models has been conducted. It shows that a finite model is of limited usage, which highlights the importance of infinite totally ordered models including probability theory. Representing the qualitative domain structure is another important issue. Bayesian networks, combining graphical representation of domain dependency and probability theory, provide a concise representation and a consistent inference formalism. An expert system QUALICON for quality control in electromyography has been implemented as a pilot study of Bayesian nets. The performance is comparable to that from human professionals. Extending the research into a large system PAINULIM in neuromuscular diagnosis shows that the computation using homogeneous net representation is unnecessarily complex. At any one time a user’s attention is directed to only part of a large net, i.e., there is ‘localization’ of queries and evidence. The homogeneous net is inefficient since the overall net has to be updated each time. Multiply Sectioned Bayesian Networks (MSBNs) have been developed to exploit localization. Reasonable constraints are derived such that a localization preserving partition of a domain and its representation by a set of subnets are possible. Reasoning takes place at only one of them due to localization. Marginal probabilities obtained are identical to those obtained when the entire net is globally consistent. When the user’s attention shifts, a new subnet is swapped in and previously acquired evidence absorbed. Thus, with β subnets, the complexity is reduced approximately to 1/β. Reducing the complexity with MSBN, the knowledge acquisition of PAINULIM has been conducted using normal hospital computers. This results in efficient cooperation with medical staff. PAINULIM was thus constructed in less than one year. An evaluation shows very good performance. Coding probability distribution of Bayesian nets in causal direction has several ad vantages. Initially the distribution is elicited from the expert in terms of probabilities of a symptom given causing diseases. Since disease-to-symptom is not the direction of daily practice, the elicited probabilities may be inaccurate. An algorithm has been derived for sequentially updating probabilities in Bayesian nets, making use of the expert’s symptom to-disease probabilities. Simulation shows better performance than Spiegeihalter’s {O, l} distribution learning.

Thesis/Dissertation

application/pdf

2008-12-16

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http://hdl.handle.net/2429/2995

Electrical and Computer Engineering

University of British Columbia

UBCV

DSpace