数据驱动的血糖模式分类与异常检测;卡塔尔世界杯8强波胆分析机器学习在1型糖尿病中的应用%A Woldaregay,Ashenafi Zebene %A Årsand,Eirik %A Botsis,Taxiarchis %A Albers,David %A Mamykina,Lena %A Hartvigsen,Gunnar %+特罗瑟大学-挪威北极大学计算机科学系，Realfagbygget, Hansine hansen vei 54，特罗瑟，挪威，47 77646444，ashenafi.z.woldaregay@uit.no %K 1型糖尿病%K血糖动态%K异常检测%K机器学习%D 2019 %7 01.05.2019背景:糖尿病是一种导致血糖(BG)调节异常的慢性代谢性疾病。通过自我管理实践，包括积极跟踪BG水平并采取适当行动，包括调整饮食和胰岛素药物，最好将BG水平维持在接近正常水平。BG异常可以定义为由于患者确切已知原因(正常原因变化)或未知原因(特殊原因变化)而导致的任何不良读数。最近，机器学习的应用被广泛地引入到糖尿病研究中，特别是血糖异常检测。然而，尽管它们越来越受欢迎，但缺乏最新的综述来实现糖尿病患者BG异常分类和检测的建模选择和策略的当前趋势。目的:本综述旨在识别、评估和分析最先进的机器学习策略及其混合系统，重点关注血糖异常分类和检测，包括血糖变异性(GV)、高血糖和低血糖，在个性化决策支持系统和BG报警事件应用的背景下，这是优化糖尿病自我管理的重要组成部分。方法:在2017年9月1日至10月1日，以及2018年10月15日至11月5日期间，通过各种网络数据库进行严格的文献检索。考虑了同行评议的期刊和文章。 Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results: The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions: Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis. %M 31042157 %R 10.2196/11030 %U //www.mybigtv.com/2019/5/e11030/ %U https://doi.org/10.2196/11030 %U http://www.ncbi.nlm.nih.gov/pubmed/31042157