After-meal blood glucose level prediction using an absorption model for neural network training

Abstract

Background: Diabetes Mellitus outpatients would benefit from a lifestyle support tool that delivers reliable short term Blood Glucose Level (BGL) predictions.

Aim: To develop a method for BGL prediction based on the baseline BGL, the insulin dosing and a dietary log.

Methods: A new training method is proposed for a neural network in which an absorption model is applied that uses the nutrient contents of meals. The numerical characteristics of the computed absorption curve are fed to the neural network as training inputs along with the applied insulin doses and BGL evolution measured by a Continuous Glucose Monitoring System. For comparison, another version of the training in which raw carbohydrate values are used as dietary inputs has also been implemented. The method was validated in a clinical trial with 5 patients using a total of 167 meals.

Results: It was found that the proposed method performed significantly better on the 60- and 120-min prediction horizons, with a Root Mean Square Error of 1.12 mmol/l and 1.75 mmol/l, respectively, and more than 96% of the predicted values falling in the 'clinically acceptable' class according to clinical practice. These results surpass those published results to which our method is directly comparable, and also those of the carbohydrate-only version (1.81 mmol/l and 2.53 mmol/l).

Conclusion: The integration of the absorption model in the training process has successfully contributed to the success of the model. Future research will focus on a new trial with more patients to verify these promising results.

Keywords: Artificial neural network; Blood glucose level prediction; Diabetes mellitus; Lifestyle support for diabetes; Meal absorption model; Outpatient care.