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Increasing transparency of recommender systems for type 1 diabetes patients

Vargheese, John Paul; Harrison, Rachel; Balbontin, Mireya Munoz; Aldea, Arantza; Brown, Daniel

Authors

Rachel Harrison

Mireya Munoz Balbontin

Arantza Aldea

Daniel Brown



Abstract

Self-management of type 1 diabetes is a challenging and complex task due the constant need for self monitoring and the diverse range of factors to consider in order to effectively regulate blood glucose levels. Recommender systems have been demonstrated to be effective for supporting patient self-management of type 1 diabetes by providing recommendations for insulin doses. Recent studies have expanded on this approach by incorporating case based reasoning within existing recommender systems for type 1 diabetes, to provide a more flexible and personalised approach to making recommendations. However, recommendations made by such systems may be ignored, even when users consider the system’s performance to be good. To address this, we propose a complimentary approach to increase the transparency of such systems through the provision of explanatory summaries that expose the reasoning process for making the recommendation. Greater transparency may increase recommendation acceptance rates and improve users’ trust and acceptability of these systems.

Presentation Conference Type Conference Paper (Published)
Conference Name Artificial Intelligence for Diabetes: 1st ECAI Workshop on Artificial intelligence for Diabetes at the 22nd European Conference on Artificial Intelligence (ECAI 2016)
Start Date Aug 30, 2016
Publication Date 2015
Deposit Date Dec 14, 2022
Pages 26-27
Book Title Artificial Intelligence for Diabetes: 1st ECAI Workshop on Artificial intelligence for Diabetes, Proceedings
Public URL http://researchrepository.napier.ac.uk/Output/2966796
Publisher URL http://ai.ijs.si/MitjaL/documents/Cvetkovic-Monitoring_patients_with_diabetes_using_wearable_sensors-Predicting_glycaemias_using_ECG_and_respiration_rate-ECAI-AID-16.pdf#page=26