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A diabetes insulin advisory system that models diabetic-specific physiology and lifestyle

Ziel

To develop the three new key components which have been identified by the clinical evaluation as necessary extensions to a Diabetic Insulin Advisory System (DLAS) to accommodate individual lifestyles and to represent diabetes-specific physiology:
- modelling of metabolic processes occurring after hypoglycaemia
- processing of data related to physical exercise
- consideration of food composition.

Diabetes is a major chronic disease widely prevalent throughout the EU and the rest of the world. It is estimated to affect 3-10% (including undiagnosed cases) of the population in Western Europe, and to require 10% out of the total health care budget including cost of diabetes related problems. It occurs in two forms, insulin-dependent diabetes (IDDM - 15% cases) and non-insulin dependent diabetes (NIDDM - 85% cases). The former requires regular insulin injections to be administered (2 injections per day) to maintain blood glucose within safe levels. Studies have shown that the percentage of patients that are able to achieve a desirable level of control is highly variable throughout Europe (10 to 70%).

Results of a recent major long-term study in the USA concluded that improvements in the control of blood glucose level lead to a substantial reduction in the rate of complications associated with diabetes (blindness, kidney failure, heart attacks, and gangrene). However, this can be difficult using conventional means due to the physiological effects of patients' varying behaviours and lifestyles, which result in day-today differences in insulin requirements and increased risk of low blood glucose concentration (hypoglycaemia).

Previous research by the investigators has led to the development of DIAS. This comprises a computer system based on advanced modelling technology incorporating Causal Probabilistic Networks (CPNs). DIAS provides advice on the amount of insulin to be injected by patients with IDDM based on the dietary data and blood-glucose measurements. Recent clinical studies performed in Denmark and the UK have provided proof of concept. They indicated a tangible clinical benefit. DIAS was able to stabilise diabetic control and recognise nocturnal hypoglycaemia. This clinical evaluation, however, highlighted the need for additional developments of the system prior to its routine use by clinicians and, ultimately, by patients in their home setting.

The research methodology will involve model formulation, implementation, and validation using CPNs. Physiological information obtained from clinical literature and from metabolic studies will be incorporated into the system as parameterised data and system knowledge. The three new DIAS extensions will be developed, verified through clinical laboratory studies.

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City University
EU-Beitrag
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Northampton Square
EC1V 0HB London
Vereinigtes Königreich

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