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

Abstract

Comunicació presentada al First Joint Workshop on AI in Health (AIH 2018), Stockholm, Sweden, July 13-14, 2018

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

This research project has been partially funded through BR-UdG Scholarship of the University of Girona granted to Joaquim Massana Raurich. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450- C2-1-R) and the European Unions Horizon 2020 research and innovation programme under grant agreement No 680708