A Bayesian network model for the diagnosis of the caring procedure for wheelchair users with spinal injury

Authors:
Maria Athanasiou;Jonathan Y. Clark
Affiliations:
Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK;Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

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Abstract

This paper describes a probabilistic causal model for the caring procedure to be followed on wheelchair users with spinal injury. Due to loss of sensation and movement caused by spinal cord injuries, the information extracted about patient findings (i.e. the signs and symptoms) can often be incomplete. This, in turn, introduces uncertainty in assessing the existence and severity of a given condition-and thus, employment of the appropriate caring procedure. Bayesian networks are a framework that enables probabilistic inference; therefore, they are useful for diagnostic reasoning and selection of the appropriate caring procedure in the face of uncertainty. The network structure and numerical parameters are based on data elicited from the qualified staff nurses and available literature of the National Spinal Injury Centre, Stoke Mandeville Hospital, Aylesbury, UK, as well as the compiled knowledge base within the DIMITRA rule-based expert system [M. Athanasiou, J.Y. Clark, DIMITRA: an online expert system for carers of paraplegics and quadriplegics, International Journal of Healthcare Technology and Management 7(5) (2006) 44-451]. We also present the model and report the results of the diagnostic performance tests using the AgenaRiskn [Agena Limited, AgenaRisk Software Package, http://www.agena.co.uk] Bayesian network package.