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More Data Matters: Improving CGM Prediction via Ubiquitous Data and Deep Learning

Diabetes mellitus is a common disease in today's population, where the insulin control system fails. It can be harmful for the patient when not treated appropriately with insulin injections. The complex functionality of the human body paired with very individual circumstances make this a hard task, even for committed patients. Modern sensor technology and personal monitoring equipment such as smartphones can help us retrieve more information about the patients blood glucose level and their habits regarding food intake and physical activities. Based on this information, we propose an assisting system giving the patient helpful advice by predicting the blood glucose level for the near future and incorporating their activities and heart rate. Deep convolutional neural networks will be used for learning the glucose level predictor. In our evaluation we see moderate improvements towards existing systems, which we think can be further improved when using more data.