A neural network approach in diabetes management by insulin administration

被引：16

作者：

Gogou G. ^{[1
]}

Maglaveras N. ^{[1
]}

Ambrosiadou B.V. ^{[2
]}

Goulis D. ^{[3
]}

Pappas C. ^{[1
]}

机构：

[1] Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Greece

[2] Department of Computer Science, University of Hertfordshire, England

[3] Imperial College School of Medicine, St. Mary's Hospital, London, UK

来源：

Journal of Medical Systems | 2001年 / 25卷 / 2期

关键词：

decision support; diabetes; insulin administration; neural networks;

D O I：

10.1023/A:1005672631019

中图分类号：

学科分类号：

摘要：

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.

引用

页码：119 / 131

页数：12

共 25 条

[1]

Lehmann E.D., Deutsch T., AIDA: A MK II Automated Insulin Dosage Advisor, J. Biomed. Eng., 15, pp. 201-242, (1993)

[2]

Sano A., Adaptive and optimal schemes for control of blood glucose levels, Biomed. Meas. Inf. Contr., 1, pp. 16-22, (1986)

[3]

Ambrosiadou V., Alevizos M., Ziakas G., Decision support in diabetes management for optimal glycaemic control, IEEE Proc Systems Man and Cybernetics, Systems Engineering in the Service of Humans. Decision Making, 5, pp. 391-397, (1993)

[4]

Schneider J., Piwernetz K., Engelbrecht R., Renner R., DIACONS-A consultation system to assist in the management of diabetes

[5]

DIAMON- An expert system to assist in the therapy of diabetes, Expert Systems and Decision Support in Medicine, pp. 44-49, (1988)

[6]

Chanoch L.H., Jovanovic L., Peterson C.M., The evaluation of a pocket computer as an aid to insulin dose determination by patients, Diabetes Care, 8, pp. 172-176, (1985)

[7]

Permick N., Rodburd D., Personal computer programs to assist with home monitoring of blood glucose and self-adjustment of insulin dosage, Diabetes Care, 9, pp. 61-69, (1986)

[8]

Dimitrov A., Nestorov I., Christov V., A two-stage adaptation scheme for computer-aided adjustment of intensified insulin dosage regimens, World Congress of Medical Physics and Biomedical Engineering Proceedings, (1994)

[9]

Miller A.S., Blott B.H., Hames T.K., Review of neural network applications in medical imaging and signal processing, Med. Biol. Eng. Comput., 30, pp. 449-464, (1992)

[10]

Maglaveras N., Stamkopoulos T., Pappas C., Strintzis M., An adaptive back-propagation neural network for real-time ischemia episodes detection. Development and performance analysis using the European ST-T database, IEEE Trans. Biomed. Engng., 45, 7, pp. 805-813, (1998)

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