2021
DOI: 10.1007/s11517-021-02433-8
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A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

Abstract: This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorpora… Show more

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Cited by 5 publications
(3 citation statements)
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“…This section presents the most recent research on data-based and hybrid hypoglycemia prediction models that only use CGM data as inputs ( 78 , 79 , 90 , 102 , 104 106 ). AR models were commonly used.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…This section presents the most recent research on data-based and hybrid hypoglycemia prediction models that only use CGM data as inputs ( 78 , 79 , 90 , 102 , 104 106 ). AR models were commonly used.…”
Section: Resultsmentioning
confidence: 99%
“…Even when only CGM glucose data below 70 mg/dL were included, similar results were obtained. Li ( 102 ) and Yu et al ( 104 ) used the model of change detection method and the Winsorization method in conjunction with the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method, respectively. The sensitivity of the former was 95.72%, and the sensitivities of the latter were 85.90, 80.36, and 78.07% when the threshold of hypoglycemia was set at 54, 70, and 79 mg/dL, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…Winsorization is a common statistic technique to deal with outliers, which replaces the extreme values with a prespecific value. The technique is applied in many fields, such as finance (Berg‐Jacobsen & Tran, 2021; Khan & Fahim, 2021), biometrics (Lan et al., 2022; Li et al., 2021), and psychology (Anderson et al., 2022; Sales et al., 2021), and is commonly incorporated the mainstream statistic software like SPSS, 2 STATA, 3 and scipy 4 package for Python. In this paper, we adopt this technique to formulate a winsorized BW maximization model as follows: [BW-R]maxxXdouble-struckEdouble-struckP̂Nfalse(min({cfalse(bold-italicxfalse)}+,c)false).$$\begin{align} \mbox{[BW-R]}\quad \max _{\bm{x}\in \mathcal {X}} \mathbb {E}_{\hat{\mathbb {P}}_N} (\min (\lbrace \tilde{c}(\bm{x})\rbrace ^+,c)).…”
Section: Model Formulationmentioning
confidence: 99%