Key result
A Random Forests predictive model for subcutaneous glucose concentration in type 1 diabetes achieved an average RMSE of 6.60 to 10.83 mg/dl for 15 to 120 minute prediction horizons.
Why the study?
Does a Random Forests regression model accurately predict subcutaneous glucose concentration in type 1 diabetes patients?
Does a Random Forests regression model accurately predict subcutaneous glucose concentration in type 1 diabetes patients?
A Random Forests regression model incorporating multivariate data and time features can predict subcutaneous glucose concentrations in type 1 diabetes with low error rates up to 120 minutes ahead.
May support glucose forecasting tools in T1D; leaves open prospective clinical validation.
In this study, an individualized predictive model of the subcutaneous glucose concentration in type 1 diabetes is presented, which relies on the Random Forests regression technique. A multivariate dataset is utilized concerning the s.c. glucose profile, the plasma insulin concentration, the intestinal absorption of meal-derived glucose and the daily energy expenditure. In an attempt to capture daily rhythms in glucose metabolism, we also introduce a time feature in the predictive analysis. The dataset comes from the continuous multi-day recordings of 27 type 1 patients in free-living conditions. Evaluating the performance of the proposed method by 10-fold cross validation, an average RMSE of 6.60, 8.15, 9.25 and 10.83 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively, was attained.
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Georga et al. (2012) studied Type 1 diabetes (n=27). Random Forests regression predictive model was evaluated on Average Root Mean Square Error (RMSE) for subcutaneous glucose concentration prediction. A Random Forests predictive model for subcutaneous glucose concentration in type 1 diabetes achieved an average RMSE of 6.60 to 10.83 mg/dl for 15 to 120 minute prediction horizons.
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