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A review of personalized blood glucose prediction strategies for T1DM patients
Author(s) -
Oviedo Silvia,
Vehí Josep,
Calm Remei,
Armengol Joaquim
Publication year - 2017
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2833
Subject(s) - personalized medicine , computer science , predictive modelling , machine learning , data mining , artificial intelligence , bioinformatics , biology
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.