Dietary glycaemic index labelling: A global perspective

The glycaemic index (GI) is a food metric that ranks the acute impact of available (digest-ible) carbohydrates on blood glucose. At present, few countries regulate the inclusion of GI on food labels even though the information may assist consumers to manage blood glucose levels. Australia and New Zealand regulate GI claims as nutrition content claims and also recognize the GI Founda-tion’s certified Low GI trademark as an endorsement. The GI Foundation of South Africa endorses foods with low, medium and high GI symbols. In Asia, Singapore’s Healthier Choice Symbol has specific provisions for low GI claims. Low GI claims are also permitted on food labels in India. In China, there are no national regulations specific to GI; however, voluntary claims are permitted. In the USA, GI claims are not specifically regulated but are permitted, as they are deemed to fall under general food-labelling provisions. In Canada and the European Union, GI claims are not legal under current food law. Inconsistences in food regulation around the world undermine consumer and health professional confidence and call for harmonization. Global provisions for GI claims/endorse-ments in food standard codes would be in the best interests of people with diabetes and those at risk

Personalized Glucose Prediction Model for Patients With Type I Diabetes

This paper represents an attempt to use machine learning techniques to personalize glucose predictions for patients with type I diabetes (T1D). The study aims at proposing a personalized model, capable to provide real-time blood glucose estimations, taking into consideration patient’s health preconditions. The proposed model represents a neural network based on the use of Self-Organized Maps (SOM).  It was elaborated using data from 5 patients with T1D, collected with help of a specially created for these purposes support system and pre-trained using a clinical dataset. The study lasted for 3 months

Diabetes Type I Self-monitoring Using Mobile Devices: Architecture Blueprint Using Cloud and Machine Learning

The modern capabilities of smartphones, cloud technologies and machine learning techniques have created possibilities of creation of innovative approaches to monitor chronic deseases. This study represents an architecture blueprint aimed at improving the efficiency of type I diabetes self-monitoring with help of mobile devices. The approach is based on a machine learning algorithm trained on diverse data sets, which offers users insights and personalized health recommendations. The platform helps improving the accuracy of diabetes tracking, provides people with immediate feedback based on history analytical data. The study highlights the merge of medical and technology fields and set a baseground for future improvements