A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

Li, Lei; Xie, Xiaolei; Yang, Jun

doi:10.1007/s11517-021-02433-8

Cited by 4 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Zhang

Yang

Zhou

2023

Front. Public Health

View full text Add to dashboard Cite

Background and objectiveHypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.Materials and methodsPubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).ResultsFrom the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.ConclusionIn this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Zhang

Yang

Zhou

2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…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:

$$\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%

From share of choice to buyers' welfare maximization: Bridging the gap through distributionally robust optimization

Liu

et al. 2023

Production and Operations Management

View full text Add to dashboard Cite

We study the product design problem where a decision maker selects the features of a product from a set of feasible options. We focus on two widely studied objectives in this field, that is, the share of choice (SOC) and buyers' welfare (BW). The two objectives are vulnerable to different types of customer preference misspecification, that is, deviation from the nominal utility distribution and effects of outliers, respectively. We formulate a distributionally robust optimization (DRO) SOC maximization model and a winsorized BW maximization model to obtain robust solutions to the two problems. Interestingly, we show that the two robust models are equivalent in a certain sense-for appropriate choices of robustness parameters, both models return the same solution. This observation has important ramifications. For instance, it indicates that a product designed to yield higher BW is more robust to the deviation from the nominal utility distribution for the SOC problem, while that with higher SOC is less sensitive to the effect of outliers for the BW problem. Last but not least, we use the equivalence to develop a new approach to solve the DRO model for the SOC problem, using a version of the winsorized BW model. Extensive numerical experiments demonstrate the superior performance of the proposed approach.

show abstract

The association between the essential metal mixture and fasting plasma glucose in Chinese community-dwelling elderly people

Wang

Lei

Wang

et al. 2023

Ecotoxicology and Environmental Safety

View full text Add to dashboard Cite

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

Cited by 4 publications

References 48 publications

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

From share of choice to buyers' welfare maximization: Bridging the gap through distributionally robust optimization

The association between the essential metal mixture and fasting plasma glucose in Chinese community-dwelling elderly people

Contact Info

Product

Resources

About