Automatic Estimation of the Nutritional Composition of Foods as Part of the GlucoseML Type 1 Diabetes SelfManagement System The daily care of type 1 diabetes has been considerably improved through the increased adoption of continuous glucose monitoring, continuous subcutaneous insulin infusion, and precise behavioral monitoring (diet, physical activity) mHealth solutions. In this study, we present the food recognition and nutrient estimation components of the GlucoseML system; a type 1 diabetes selfmanagement system relying on shortterm predictive analytics of the glucose trajectory. A computervisionbased approach is outlined combining image processing and machine learning to plate detection, food segmentation, food recognition and volume estimation of a plateu0027s content. The systematic collection of an annotated Greek food images dataset allows the evaluation of the proposed methodology.

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.