A neural network approach in diabetes management by insulin administration.
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 other 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, classifying correctly 92% of the testing cases.
Gogou
G
G
Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Greece.
Maglaveras
N
N
Ambrosiadou
B V
BV
Goulis
D
D
Pappas
C
C
D000328:Adult; D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D002648:Child; D020000:Decision Support Systems, Clinical; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy*; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D015444:Exercise; D006115:Greece; D006801:Humans; D007328:Insulin / Q000008:administration & dosage*; D008019:Life Style; D016571:Neural Networks, Computer; D014584:User-Computer Interface
D001786:Blood Glucose; D007328:Insulin