A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia.
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 ana...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 11/12; pp. 2311 - 2325 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153319070&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153319070 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2021 vid: 59 iid: 11/12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153319070 152725606 153319070 NLM34591245 10.1007/s11517-021-02433-8 NLM34591245 153319070 ppf: 2311 ppct: 14 formats: fmt: @attributes: type: P tig: atl: A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia. aug: au: Li, Lei Xie, Xiaolei Yang, Jun affil: School of Reliability and Systems Engineering, Beihang University, 100191, Beijing, People's Republic of China sug: subj: Hypoglycemia Diagnosis Diabetes Mellitus Diabetes Mellitus, Type 1 Hyperglycemia Diagnosis Blood Glucose Self-Monitoring Blood Glucose Questionnaires ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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