Abstract
Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE5 day) not used during ANN training. For BGL predictions of up to 1 hour aRMSE5 dayof (±SD)0.15±0.04 mmol/L was observed. For BGL predictions up to 10 hours, aRMSE5 dayof (±SD)0.14±0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.
Highlights
The population with diabetes in 2000 was estimated to be 171 million worldwide
For Type 1 diabetics, the most common method for management is through with monitoring the blood glucose level (BGL) using fingerprick blood tests taken several times a day, and adjusting insulin doses based on these readings
The error in the prediction was calculated as the RMSE between the target BGL and the predicted BGL from the artificial neural networks (ANNs) over the prediction period, see (4), for 5 days of data not used during training
Summary
The population with diabetes in 2000 was estimated to be 171 million worldwide. It has been shown that, through good management of diabetes, its associated debilitating and costly complications can be reduced. For Type 1 (insulin-dependent) diabetics, the most common method for management is through with monitoring the blood glucose level (BGL) using fingerprick blood tests taken several times a day, and adjusting insulin doses based on these readings. Nonlinear, and complex condition such as diabetes this can be far from satisfactory. Factors such as insulin type and dose, diet, stress, exercise, illness, and pregnancy all have significant influences on the BGL. Management may be compromised through lack of data and, for some patients, an inability to interpret data adequately
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