Nutritionist based on Deep Learning

As the growth of the Internet has become very rapid, telemedicine can be performed efficiently. One important issue of telemedicine is nutrition recommendations for patients who live in long-distance areas far away from hospitals. Diet imbalance of people has become a very serious issue that the occurrences of obesity, metabolic syndrome, diabetes, and even cancer have raised. However, according to the data of the Health and Welfare Department, there are only 1663 dietitians in all hospitals in Taiwan in 2015. Undoubtedly it is a significant loading burden for these few dietitians. Therefore, this proposal aims to design and develop a virtual nutritionist and provide effective diet. Our data has established a complete database based on the recommendations of nutritionists. The database contains most of the food types. The data is trained using the model framework we have established based on the past analysis results of nutritionists, and the items and quantities that users must eat in each time period are accurately recommended and it include breakfast, lunch, and dinner. In the final stage, the training process can clearly show that the model is accurately trained, and the generated menu can be compared with the nutritionist to have good results. Based on the results of the nutritional assessment, the virtual nutritionists will provide each patient with good dietary advice and dietary guardians. The goal is to (1) assist nutritionists in nutritional screening, thereby saving time and energy; (2) inquiring unlimited nutritional information; (3) allowing people to experience advanced medical services and quality.