(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

中文翻译：

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 趋势与挑战


(1)背景：在过去几年中，用于预测低血糖的机器学习技术的使用大幅增加。低血糖是指糖尿病患者的血糖降至临界水平以下。这可能会导致认知能力丧失、癫痫发作，在极端情况下甚至导致死亡。在几乎一半的严重病例中，低血糖是突然发生的，而且基本上是无症状的。糖尿病患者无法预测和干预低血糖事件的发生通常会导致危机。因此，低血糖的预测是改善糖尿病患者生活质量的重要一步。本文的目的是回顾利用机器学习在低血糖预测领域所做的工作，并探讨研究人员在该领域面临的最新趋势和挑战； (2)方法：查阅PubMed和Google Scholar的文献。为此目的，我们检索了过去五年的手稿。初选论文903篇，最终入围详细评审论文57篇； （3）结果：对入围手稿的彻底剖析提供了基于两个类别的作品之间的有趣划分：低血糖预测和低血糖检测。整个审查是在保持这种分类区别的角度进行的，同时对用于预测低血糖的机器学习方法、训练数据类型和预测范围进行了全面概述。