A review of personalized blood glucose prediction strategies for T1DM patients

Oviedo, Silvia; Vehí, Josep; Calm, Remei; Armengol, Joaquim

doi:10.1002/cnm.2833

Cited by 282Diabetes Management and ResearchAdvanced Control Systems OptimizationArtificial Intelligence in Healthcare

A review of personalized blood glucose prediction strategies for T1DM patients

Oviedo, Silvia, Vehí, Josep, Calm, Remei, Armengol, Joaquim·2017·International Journal for Numerical Methods in Biomedical Engineering

DOI 10.1002/cnm.2833

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Abstract

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.

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Crossref · 10.1002/cnm.2833

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Algorithms that use CGM data plus external inputs, such as ingested carbohydrates, injected insulin and physical activity, have been shown to enhance prediction performance compared to predictions using only CGM data.
“While, others use CGM data plus external inputs, such as the amount of ingested carbohydrates, injected insulin and physical activity [52]. These have been shown to enhance the performance of predictions when compared to predictions using only CGM data [53].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [52]
Subject-specific glucose prediction algorithms train a different model for each subject to account for the large inter-individual variability characterizing individuals with T1D.
“Another option is to develop subject-specific algorithms, where a different model is trained for each subject, in order to take into account the large inter-individual variability characterizing individuals with T1D [52].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [52]
Most glucose prediction algorithms in the literature approach glucose prediction as a regression problem, seeking to predict interstitial glucose concentration with a prediction horizon typically below 60 min, with 30 min the most frequent.
“Most of the prediction algorithms in the literature approach the problem of glucose prediction as a regression problem, in other words they seek to predict the value of interstitial glucose concentration with a prediction horizon (PH) typically below 60 min (30 min is the most frequent PH value) [52].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [52]

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Artificial Pancreas: from in-silico to in-vivo∗∗This work was supported by the Fondo per gli Investimenti della Ricerca di Base project Artificial Pancreas: In Silico Development and In Vivo Validation of Algorithms for Blood Glucose Control funded by Italian Ministero dell'Istruzione, dell'Universitä e della Ricerca.
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Authors: Oviedo, S., Vehí, J., Calm, R., & Armengol, J.
Affiliations: Universitat de Girona (ES)
Institut d'Informàtica i Aplicacions Universitat de Girona, Campus Montilivi Edifici P4 17071 Girona Spain
ORCID: Calm, Remei
Armengol, Joaquim
Venue: International Journal for Numerical Methods in Biomedical Engineering
Publisher: Wiley
Published: 2016-10-28
Volume: 33
Issue: 6
ISSN: 2040-7939 (ISSN-L)
2040-7947
DOI: 10.1002/cnm.2833
OpenAlex: W2523526638
Semantic Scholar: 8246ce31245b787e63f8a459f3829443cab32258
Cited by: 279
References: 135
Citations by year: 2026: 15 · 2025: 26 · 2024: 29 · 2023: 31 · 2022: 34 · 2021: 46 · 2020: 34 · 2019: 38 · 2018: 18 · 2017: 8
FWCI: 3.14
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: International Journal for Numerical Methods in Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
Universitat de Girona Digital Repository (Universitat de Girona) (publishedVersion)
Repositori UJI (Universitat Jaume I) (publishedVersion)
RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion)
LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, public-domain)
Funders: Spanish Government (ES-2014-068289)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: blood glucose prediction, type 1 diabetes, personalized prediction models, diabetes risk prediction, model classification
MeSH terms: Blood Glucose, Diabetes Mellitus, Type 1, Humans, Insulin

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A review of personalized blood glucose prediction strategies for T1DM patients

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