Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes

Type 1 diabetes mellitus (T1D) is a chronic disease that, if not treated properly, can lead to serious complications. We propose a layered meta-learning approach based on multi-expert systems to predict adverse events in T1D. The base learner is composed of three deep neural networks and exploits only continuous glucose monitoring data as an input feature. Each network specializes in predicting whether the patient is about to experience hypoglycemia, hyperglycemia, or euglycemia. The output of the experts is passed to a meta-learner to provide the final model classification. In addition, we formally introduce a novel parameter,
$\alpha $
, to evaluate the advance by which a prediction is performed. We evaluate the proposed approach on both a public and a private dataset and implement it on an edge device to test its feasibility in real life. On average, on the Ohio T1DM dataset, our system was able to predict hypoglycemia events with a time gain of 22.8 minutes, hyperglycemia ones with an advance of 24.0 minutes. Our model not only outperforms presented models in the literature in terms of events predicted with sufficient advance, but also with regard to the number of false positives, achieving on average 0.45 and 0.46 hypo- and hyperglycemic false alarms per day, respectively. Furthermore, the meta-learning approach effectively improves performance in a new cohort of patients by training only the meta-learner with a limited amount of data. We believe our approach would be an essential ally for the patients to control the glycemic fluctuations and adjust their insulin therapy and dietary intakes, enabling them to speed up decision-making and improve personal self-management, resulting in a reduced risk of acute and chronic complications. As our last contribution, we assessed the validity of the approach by exploiting only blood glucose variations as well as in combination with the information of the insulin boluses, the skin temperature, and the galvanic skin response. In general, we have observed that providing other information but CGM leads to slightly lower performances with respect to considering CGM alone.