Résumé
The advent of big data and artificial intelligence opens new perspectives for diabetes mellitus monitoring and management. Yet, complexity and uniqueness of the human body hardly allow to define a general statistical approach able to accurately predict blood glucose variations in all patients. Our technology, MIND, is a personalized, patient-specific blood glucose level (BGL) prediction service integrated in a diabetes management platform. For each patient, a machine Learning model is created using his/her historical data, including BGL and insulin inputs collected through wide-spread devices and meals recorded by the patient when available. The CDDIAB study conducted in 2018 demonstrated that our prediction technology is accurate enough to allow safe therapeutical decisions ; an extension of this study conducted in 2019 showed that accurate BGL predictions drive better decision making on treatment options than patient alone. The positive outcomes of this study triggered new research cases. First, the technology is tested on another patient cohort to assess its robustness; then, the confidence in the prediction provided is studied. The second point is essential for large-scale industrialization. Hence the study of a confidence index and an envelope curve, to provide visual insights of the accuracy and an additional security on the predictions. Based on our results, the next challenge is to predict accurate bolus doses given the historical data and the predicted BGL. Another challenge is to build a system able to detect and reconstruct meals, thus meal management would be streamlined by avoiding manual inputs.