Personalized machine learning algorithm based on shallow network and error imputation module for an improved blood glucose prediction

Real-time forecasting of blood glucose (BG) levels has the potential to drastically improve management of Type 1 Diabetes, a widespread chronic disease affecting the metabolic system. Most notably, if hypo or hyperglycemia episodes (i.e. glycemic excursion below or above a safe range) could be accurately predicted, then the patient could be timely warned, thus enabling proactive countermeasures to avoid these dangerous conditions. In this work, a novel personalized algorithm for the real-time forecasting of BG is developed by combining the output of a shallow feed forward neural network with an error imputation module composed by an ensemble of trees. Past glucose readings as well as insulin, meals and work/sleep time information are carefully handled to train and boost the prediction performance of the algorithm. The root mean square error over the 6 subjects achieves a mean value of 18.69 mg/dL and 32.43 mg/dL for 30- and 60-minute prediction horizon respectively.