No description available.

The condition of the fetus during labour is inferred from the continuous plot of fetal heart rate and uterine contractions (cardiotocogram, CTG). This can be _ difficult to interpret which results in both unnecessary intervention and a failure to intervene when necessary causing potentially preventable neurological damage and mortality. Conventional computing approaches have not been successful in addressing these problems. This is perhaps because the correct interpretation of fetal condition requires physiological knowledge, considerable practical experience and knowledge of the specific patient. The work described in this thesis is concerned with the investigation of artificial intelligence techniques to assist in the interpretation of fetal condition and advise on labour management. A fundamental investigation examined the performance of five types of scalp electrodes for obtaining the fetal electrocardiogram (ECG), from which heart rate is derived, and examined the factors which hamper fetal ECG data acquisition. New methods were developed to classify the important features from the CTG and included an investigation using neural networks. Other CTG features were classified using novel numerical algorithms developed closely with experts. An expert system, guided by a database of rules obtained from experts, was used to process and interpret changes in the CTG features by taking account of patient specific information. This hybrid approach was adopted to improve performance and reliability. After two internal evaluations had found the system obtained a performance comparable with local experts, an extensive external validation was undertaken. This study involved 17 experts from 16 leading centres within the UK. Each expert and the system reviewed 50 cases twice, at least one month apart which contained those considered most difficult to interpret selected from a database of 2400 high risk labours. A novel method was developed to present all the relevant clinical information in a way which approximated the clinical situation. The reviewers scored each 15 minutes of recording according to the concern they had for the fetus and the management they considered appropriate. In this respect, this is the first reported study to examine the performance of expert obstetricians in the management of labour. A new method was derived to measure the agreement between the scores obtained and is applicable to other areas where it is required to measure the similarity between time related sequences. This study found that the experts agreed well and were consistent in their management of the cases. The system was indistinguishable from the experts, except it was more consistent, even when used by an engineer with little knowledge of labour management. This study has shown that expertise in fetal monitoring is achievable in which case the current evidence suggests that this is not being adequately transferred to clinicians. The challenge remains to formulate a method to effectively transfer knowledge to the labour ward and thereby address the real and practical problems which face fetal monitoring today. This study demonstrates that intelligent systems could provide the vehicle to achieve this. I dedicate this work to the memory of my father, Bradley Kenneth Keith with a hope that he always believed it possible. I know he would have had some interesting comments to make and I sadly miss the opportunity of discussing them with him. I also dedicate this work to my mother for always being there, and to my wife Michelle for her unwavering support, patience and most of all her encouragement throughout this work.

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This research has identified the performance measures congruent with each model during a business life cycle. Questionnaire surveys have also been carried out to complement and validate the theoretical models. The results of the survey generally confirm the expected measures derived from the theoretical models. The learning process for these newer industrial business organisations can be greatly reduced if the expertise and experience of the established manufacturing business organisations is made readily available. This is the motivation for this research and the methodology which has been developed. The research also proposes use of a knowledge based expert decision support system to encapsulate the methodology, and the wealth of expert knowledge in the domain of performance measures. A prototype knowledge based expert decision support system has been developed to test the concept. It is hoped that this research has achieved its aim to provide a new contribution in the manufacturing business organisation strategy domain and to the improvement of managerial productivity and effectiveness through better use of performance measures.

No description available.

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xiii, 151 p. : ill. (some col.), col. maps. Includes abstract. Includes bibliographical references (p. 104-107).

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 190-201). Also available via the Internet

Thesis (Ph. D.)--University of Washington, 1991. / Vita. Includes bibliographical references (leaves [116]-120).