Potential predictors of type2 diabetes risk: machine learning, synthetic data and wearable health devices Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statistical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationallycheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.

Model Fusion to Enhance the Clinical Acceptability of LongTerm Glucose Predictions This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making longterm glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 insilico type1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feedforward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the rootmeansquared error of the prediction, the rootmeansquared error of the predicted variation, and the continuous glucoseerror grid analysis. By making a successful tradeoff between prediction accuracy and predictedvariation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.