@文章{信息:doi/10.2196/34699，作者=“Tsichlaki, Stella and Koumakis, Lefteris and Tsiknakis, Manolis”，标题=“1型糖尿病低血糖预测算法:系统综述”，期刊=“JMIR糖尿病”，年=“2022”，月=“7月”，日=“21”，卷=“7”，数=“3”，页=“e34699”，关键词=“1型糖尿病;低血糖症;预测模型;连续血糖监测;心率变异性;背景:糖尿病是一种慢性疾病，需要定期监测和自我管理患者的血糖水平。1型糖尿病(T1D)患者如果接受适当的糖尿病治疗，可以过上富有成效的生活。然而，血糖控制不严格可能会增加发生低血糖的风险。这种情况的发生可能是由于各种原因，如服用额外剂量的胰岛素，不吃饭，或过度运动。低血糖的症状主要从轻微的烦躁到更严重的情况，如果不及时发现。 Objective: In this review, we aimed to report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on T1D. Methods: A systematic literature search following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed focusing on the PubMed, GoogleScholar, IEEEXplore, and ACM Digital Library to find articles on technologies related to hypoglycemia detection in patients with T1D. Results: The presented approaches have been used or devised to enhance blood glucose monitoring and boost its efficacy in forecasting future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected 19 predictive models for hypoglycemia, specifically on T1D, using a wide range of algorithmic methodologies, spanning from statistics (1.9/19, 10{\%}) to machine learning (9.88/19, 52{\%}) and deep learning (7.22/19, 38{\%}). The algorithms used most were the Kalman filtering and classification models (support vector machine, k-nearest neighbors, and random forests). The performance of the predictive models was found to be satisfactory overall, reaching accuracies between 70{\%} and 99{\%}, which proves that such technologies are capable of facilitating the prediction of T1D hypoglycemia. Conclusions: It is evident that continuous glucose monitoring can improve glucose control in diabetes; however, predictive models for hypo- and hyperglycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mobile health in T1D. Prospective studies are required to demonstrate the value of such models in real-life mobile health interventions. ", issn="2371-4379", doi="10.2196/34699", url="https://diabetes.www.mybigtv.com/2022/3/e34699", url="https://doi.org/10.2196/34699", url="http://www.ncbi.nlm.nih.gov/pubmed/35862181" }