AUTHOR=Zaitcev Aleksandr , Eissa Mohammad R. , Hui Zheng , Good Tim , Elliott Jackie , Benaissa Mohammed 

TITLE=Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study

JOURNAL=Frontiers in Clinical Diabetes and Healthcare

VOLUME=Volume 4 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2023.1095859

DOI=10.3389/fcdhc.2023.1095859

ISSN=2673-6616

ABSTRACT=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.