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

Frontiers in Clinical Diabetes and Healthcare

Clinical Diabetes and Healthcare

Zheng Hui¹  Mohammed Benaissa¹  Mohammad R. Eissa¹  Aleksandr Zaitcev¹  Tim Good¹  Jackie Elliott² 

[1] Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom;Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom;Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals NHS FT, Sheffield, United Kingdom;

关键词: biomedical informatics;    classification algorithms;    machine learning;    medical expert systems;    statistical analysis;    hypoglycemia;    exercise;    physical activity;   

DOI  :  10.3389/fcdhc.2023.1095859

 received in 2022-11-11, accepted in 2023-03-16,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundHypoglycemia 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.MethodsReasons for 1885 hypoglycemia events were labeled by 54 participants with type 1 diabetes over a 21 months period. A broad range of possible predictors were extracted describing a hypoglycemic episode and the subject’s general self-care from participants’ routinely collected data on the Glucollector, their diabetes management platform. Thereafter, the possible hypoglycemia reasons were categorized for two major analysis 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.ResultsPhysical activity contributed to 45% of hypoglycemia reasons on the real world collected data. The statistical analysis provided a number of interpretable predictors of different hypoglycemia reasons based on self-care behaviors. The classification analysis showed the performance of a reasoning system in practical settings with different objectives under F1-score, recall and precision metrics.ConclusionThe data acquisition characterized the incidence distribution of the various hypoglycemia reasons. The analyses highlighted many interpretable predictors of the various hypoglycemia types. Also, the feasibility study presented a number of concerns valuable in the design of the decision support system for automatic hypoglycemia reason classification. Therefore, automating the identification of the causes of hypoglycemia may help objectively to target behavioral and therapeutic changes in patients' care.

【 授权许可】

Unknown   
Copyright © 2023 Zaitcev, Eissa, Hui, Good, Elliott and Benaissa