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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 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.

Towards Non-Invasive Extraction and Determination of Blood Glucose Levels

“…NNs have the capability to train and process complex functions in many applications including identification, pattern recognition, speech processing, classification, and control systems [ 21 ]. NNs are implemented for diabetic diagnosis [ 22 ], for analyzing blood glucose range as a primary indicator of the diabetic condition, in addition to diabetic management and associated insulin administration [ 22 ]. NNs reveal their utility and efficiency in solving problems that require prediction, clinical diagnosis, pattern recognition, and/or image analysis.…”

“…A system is designed in [ 22 ] to provide testing of diabetic abnormalities, where NNs are used in the early stages of diabetic diagnosis. Input parameters to the NN classifier include the number of times a patient has been pregnant, their plasma glucose concentration, blood pressure, body mass index, and insulin production level [ 22 ]. Results of the research revealed the NNs capable of learning patterns corresponding to diabetic symptoms of an individual, achieving an accuracy of 98% [ 22 ].…”

Specification and Verification of Medical Monitoring System Using Petri-nets

“…Gogou et al . [ 11 ] stated that using a FPN to model fuzzy rule based reasoning provides a couple of advantages:…”

“…Gogou et al . [ 11 ] used the neural network for an INS administration system to manage the diabetics. Ward and Martin[ 19 ] used a fuzzy inference system to propose a glucose regulation model.…”

Electronic Healthcare Records: an essential part of Health Telematics Applications

“…Concepts developed in the DIADOQ and the ByMedCard system were used to implement the DCDS in the DIABCARD Core System (Fig. 4) [12], the electronic patient record for all 8 sites. First evaluation of the system has been performed leading to an optimised user and functional adaptation.…”