A review of personalized blood glucose prediction strategies for T1DM patients

被引：176

作者：

Oviedo, Silvia ^{[1
]}

Vehi, Josep ^{[2
]}

Calm, Remei ^{[2
]}

Armengol, Joaquim ^{[2
]}

机构：

[1] Univ Girona, Inst Informat & Aplicac, Parc Cient & Tecnol, Girona 17003, Spain

[2] Univ Girona, Inst Informat & Aplicac, Campus Montilivi,Edifici P4, Girona 17071, Spain

来源：

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING | 2017年 / 33卷 / 06期

关键词：

artificial pancreas; blood glucose prediction; data-driven BG prediction models; hybrid BG prediction models; physiological BG prediction models; predictive models; LOOP INSULIN DELIVERY; TYPE-1; DIABETES-MELLITUS; ARTIFICIAL PANCREAS; POSTPRANDIAL RESPONSE; MONITORING SENSORS; TIME PREDICTION; NEURAL-NETWORK; YOUNG-PEOPLE; IN-SILICO; HOME-USE;

D O I：

10.1002/cnm.2833

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.

引用

页数：21

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