A Predictive Model Incorporating the Change Detection and Winsorization Methods for Alerting Hypoglycemia and Hyperglycemia

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2021 Sep 30

PMID 34591245

Citations 2

Authors

Lei Li

Xiaolei Xie

Jun Yang

Affiliations

Soon will be listed here.

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 incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.

Citing Articles

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

Yu X, Yang Z, Wang X, Sun X, Shen R, Li H BMC Med Inform Decis Mak. 2024; 24(1):378.

PMID: 39696373 PMC: 11653906. DOI: 10.1186/s12911-024-02761-3.

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

Zhang L, Yang L, Zhou Z Front Public Health. 2023; 11:1044059.

PMID: 36778566 PMC: 9910805. DOI: 10.3389/fpubh.2023.1044059.

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