Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs

Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.

Authors

Eyal Dassau (ORCID: https://orcid.org/0000-0001-5333-6892)
Başak Özaslan (ORCID: https://orcid.org/0000-0002-8537-1892)
Eleonora M. Aiello (ORCID: https://orcid.org/0000-0001-5129-8829)
Francis J. Doyle (ORCID: https://orcid.org/0000-0002-3293-9114)
Emilia Fushimi (ORCID: https://orcid.org/0000-0001-5820-3108)

Institutions

Harvard University (US)
Harvard University Press (US)

Publication Details

Journal: IEEE Transactions on Biomedical Engineering
Published: 2025-01-16
DOI: https://doi.org/10.1109/tbme.2025.3530711
Citations: 3
Primary Topic: Diabetes Management and Research
Type: article
Field-Weighted Citation Impact: 3.88

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article

Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs

Eyal Dassau, Başak Özaslan, Eleonora M. Aiello, Francis J. Doyle et al.

Jan 16, 2025

3 citations

IEEE Transactions on Biomedical Engineering

Diabetes Management and Research

3.88

article

Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs

Eyal Dassau, Başak Özaslan, Eleonora M. Aiello, Francis J. Doyle, Emilia Fushimi

January 16, 2025article en

3 citations

Abstract

Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.

IEEE Transactions on Biomedical EngineeringVol. 72(6)

Harvard University (US), Harvard University Press (US)

Zero hunger

Openalex Percentile: Top 8%

Diabetes Management and Research

3.88

DOI Source OpenAlex

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