Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study - Zaitcev, Aleksandr; Eissa, Mohammad R.; Hui, Zheng; Good, Tim; Elliott, Jackie; Benaissa, Mohammed (Frontiers in Clinical Diabetes and Healthcare, 2023)
Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients. Therefore, there is a clear motivation to automate this process using a supervised machine learning paradigm. This manuscript presents a feasibility study of automatic identiﬁcation of hypoglycemia causes, consisting of two major sections - statistical analysis of relationships between the data features of self-care and hypoglycemia reasons, and classiﬁcation analysis investigating the design of an automated system to determine the reason for hypoglycemia. Our results highlight a number of interpretable hypoglycemia class predictors and performance of such a reasoning system in practical settings.