A Neural Network Approach in Diabetes Management by Insulin Administration

Authors:
G. Gogou;N. Maglaveras;B. V. Ambrosiadou;D. Goulis;C. Pappas
Affiliations:
Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Greece;Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Greece;Department of Computer Science, University of Hertfordshire, England;Imperial College School of Medicine, St. Mary's Hospital, London, UK;Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Greece
Venue:
Journal of Medical Systems
Year:
2001

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Abstract

Diabetes management by insulin administration is based on medical experts' experience, intuition, and expertise. As there is very little information in medical literature concerning practical aspects of this issue, medical experts adopt their own rules for insulin regimen specification and dose adjustment. This paper investigates the application of a neural network approach for the development of a prototype system for knowledge classification in this domain. The system will further facilitate decision making for diabetic patient management by insulin administration. In particular, a generating algorithm for learning arbitrary classification is employed. The factors participating in the decision making were among others diabetes type, patient age, current treatment, glucose profile, physical activity, food intake, and desirable blood glucose control. The resulting system was trained with 100 cases and tested on 100 patient cases. The system proved to be applicable to this particular problem, classifing correctly 92% of the testing cases.