使用自我记录数据检测1型糖尿病患者感染发生率(第一部分):卡塔尔世界杯8强波胆分析一种个性化数字传染病检测系统的新框架[A Woldaregay,Ashenafi Zebene %A Launonen,Ilkka Kalervo %A Årsand,Eirik %A Albers,David %A holubov，Anna %A Hartvigsen,Gunnar %+特罗姆瑟大学-挪威北极大学计算机科学系，Hansine hansen veg 54，科学大楼Realfagbygget, A124办公室，挪威特罗姆瑟，47 46359333，ashenafi.z.woldaregay@uit.no %K 1型糖尿病%K自我记录健康数据%K感染发生率%K决策%K传染病暴发%K公共卫生监测%D 2020 %7 12.8.2020 %9背景:1型糖尿病是由胰腺细胞胰岛素分泌不足引起的血糖代谢紊乱的慢性疾病。在1型糖尿病患者中，高血糖常发生在感染发生时。尽管患者越来越多地收集自己的数据，但没有确凿的发现揭示感染发生率对血糖动力学关键参数的影响，以支持开发数字传染病检测系统的努力。目的:本研究旨在回顾性分析感染发生率的影响，并确定可有效用作开发感染检测算法的输入变量的最佳参数，并为如何使用1型糖尿病患者的自我记录数据作为次要信息来源设计和开发数字传染病检测系统提供总体框架。方法:我们回顾性分析了3名1型糖尿病患者纵向记录的10患者年的高精度自我记录数据。从大量参与者那里获得如此丰富和庞大的数据集是极其昂贵和难以获得的，如果不是不可能的话。数据集包括血糖、胰岛素、碳水化合物和自我报告的感染事件。我们研究了在指定时间范围内(每周、每天和每小时)关键血糖参数的时间演变和概率分布。 Results: Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions: We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field. %M 32784178 %R 10.2196/18911 %U //www.mybigtv.com/2020/8/e18911 %U https://doi.org/10.2196/18911 %U http://www.ncbi.nlm.nih.gov/pubmed/32784178