典型文献
Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models
文献摘要：
This paper proposes five indicators to evaluate the effectiveness and viability for adverse glycemic events detection based on predicted blood glucose(BG)values.False negative rate(FNR)and false positive rate(FPR)are defined to evaluate whether it can detect adverse glycemic events(AGEs)based on the predicted value.The temporal overlap(TO)and time difference(TD)are proposed to evaluate whether the predicted model can capture the accurate time duration of AGEs.The sum of squared percent(SSP)measures comprehensive similarity between prediction values and true values.We examined 328 patients with type 2 diabetes,containing real continuous glucose monitoring data with 5-minute time intervals.Autoregressive integrated moving average model has lower FNR and FPR.The gated recurrent unit has better temporal behavior where the mean TO with standard deviation is calculated as 0.84±0.18,and the mean TD with standard deviation is(4.39±4.01)min.Neural models have better effects on SSP(for hypoglycemia,long-short tern memory possesses 0.149 and 0.246).These five indicators are able to evaluate whether we can detect abnormal BG levels and reveal the temporal behavior of AGEs effectively.The proposed neural predictive models have more promising application in AGE detection.
文献关键词：
中图分类号：
作者姓名：
LU Guannan;WANG Mengling;FOX Tamara;JIANG Peng;JIANG Fusong
作者机构：
Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,Shanghai 200237,China;INTERGRIS Mental Health Center,Spencer,OK 73084,USA;Shanghai Jiao Tong University Affiliated Sixth People's Hospital,Shanghai 200237,China
文献出处：
引用格式：
[1]LU Guannan;WANG Mengling;FOX Tamara;JIANG Peng;JIANG Fusong-.Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models)[J].上海交通大学学报（英文版）,2022(04):498-504
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AB值：
0.604692
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