19 research outputs found
Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes
Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models
The quality of life of diabetic patients can be enhanced by devising a personalized control algorithm, integrated within an artificial pancreas, capable of dosing the insulin. A key action in the building of this artificial device is to conceive an efficient algorithm for forecasting future glucose levels. Within this paper, an evolutionary-based strategy, i.e., a Grammatical Evolution algorithm, is devised to deduce a personalized forecasting model to evaluate blood glucose values in the future on the basis of the past glucose measurements, and the knowledge of the basal and infused insulin levels and of the food consumption. The aim is to discover models that are not only interpretable but also with low complexity to be used within a control algorithm that is the main element of the artificial pancreas. A real-world database composed by Type 1 diabetic patients has been employed to evaluate the proposed evolutionary automatic procedure
Comparing the PaGMO Framework to a De-randomized Meta-Differential Evolution on Calculation and Prediction of Glucose Levels
Supplementary Material for: Mindfulness-Based Attention Training Improves Cognitive and Affective Processes in Daily Life in Remitted Patients with Recurrent Depression: A Randomized Controlled Trial
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