A comparison of various deep learning models on blood glucose management of type 1 diabetes: A brief review

Abstract

Type 1 diabetes has become one of the most common chronic diseases nowadays because patients pancreas cannot produce sufficient insulin, which helps the blood sugar to enter the cells, which will cause them to build up in the bloodstream and leads to complications and dis-eases. Therefore, a basal-bolus insulin therapy that contains daily insulin injections has be-come routine for patients with type 1 diabetes to help regulate the blood sugar level. To better monitor the blood glucose level, digital health monitors are becoming the trending method for type 1 diabetes patients. Meanwhile, all the data generated by the digital monitoring devices made researchers realize that deep learning algorithms could be implanted to help the device better predict a patients blood sugar levels. In this paper, we aim to present a review of testing several state-of-the-art deep-learning models on blood glucose prediction. We have identified a literature search and focused on the deep-learning algorithms for glucose management. After detailed explanations of each model, we employ them on one mutual dataset to identify the direct prediction results of each model and compare the pros and cons of these models accord-ing to the results report. While all these models have the most advanced frameworks, the lack of feature varieties and data availability becomes their limitations. However, followed by the increased focus on the digital health field, these challenges might soon get resolved, which leads to more comprehensive models that could be further deployed in clinical conditions.