Case base maintenance of a personalized insulin dose recommender system for Type 1 Diabetes Mellitus

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With the goal of supporting people su ering Type 1 Diabetes Mellitus (T1DM), some mobile applications are being developed based on arti cial intelligence techniques. Some of these applications are based on Case-Based Reasoning methodologies (CBR) due to the advantage regarding a personal, adapted recommendation. However, the amount and quality of the cases in the CBR system will threat the system outcome. Most of the maintenance methods developed deals with classi cation tasks, while recommending an insulin dose (bolus) involves a regression task. In this paper, a new maintenance method presented, with the particularity of dealing with a regression tasks. The method is applied over the Pepper insulin dose recommender system, and tested using the UVA/Padova simulator, exhibiting the improvements of the proposal in terms of both, the person health and the case-base size ​
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