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BackgroundArtificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research.MethodsThis study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research.ResultsResearch results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly.ConclusionsThe platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.

The food calorie estimation system (FCES) is designed to record dietary information for diabetic patients to monitor their dietary intake to estimate the number of calories they are consuming. Deep learning technologies have recently been used for FCESs. In this work, we use the neural network for the pattern recognition of food images to calculate the number of calories. In contrast to the traditional convolutional neural network, we build a semantic segmentation network model based on SegNet + MobileNet to segment the food images and extract the area feature of food images. By determining the corresponding relationship between the area feature of the food image and the food calorie value, the number of calories in the food can be estimated and realized. The experimental results show that the accuracy of food recognition reached 97.82% and that of calorie estimation was above 84.95%.

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