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Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-s...

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Autores principales: Thamotharan, Padmapritha, Srinivasan, Seshadhri, Kesavadev, Jothydev, Krishnan, Gopika, Mohan, Viswanathan, Seshadhri, Subathra, Bekiroglu, Korkut, Toffanin, Chiara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056736/
https://www.ncbi.nlm.nih.gov/pubmed/36983097
http://dx.doi.org/10.3390/jcm12062094
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author Thamotharan, Padmapritha
Srinivasan, Seshadhri
Kesavadev, Jothydev
Krishnan, Gopika
Mohan, Viswanathan
Seshadhri, Subathra
Bekiroglu, Korkut
Toffanin, Chiara
author_facet Thamotharan, Padmapritha
Srinivasan, Seshadhri
Kesavadev, Jothydev
Krishnan, Gopika
Mohan, Viswanathan
Seshadhri, Subathra
Bekiroglu, Korkut
Toffanin, Chiara
author_sort Thamotharan, Padmapritha
collection PubMed
description Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.
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spelling pubmed-100567362023-03-30 Human Digital Twin for Personalized Elderly Type 2 Diabetes Management Thamotharan, Padmapritha Srinivasan, Seshadhri Kesavadev, Jothydev Krishnan, Gopika Mohan, Viswanathan Seshadhri, Subathra Bekiroglu, Korkut Toffanin, Chiara J Clin Med Article Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%. MDPI 2023-03-07 /pmc/articles/PMC10056736/ /pubmed/36983097 http://dx.doi.org/10.3390/jcm12062094 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Thamotharan, Padmapritha
Srinivasan, Seshadhri
Kesavadev, Jothydev
Krishnan, Gopika
Mohan, Viswanathan
Seshadhri, Subathra
Bekiroglu, Korkut
Toffanin, Chiara
Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
title Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
title_full Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
title_fullStr Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
title_full_unstemmed Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
title_short Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
title_sort human digital twin for personalized elderly type 2 diabetes management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056736/
https://www.ncbi.nlm.nih.gov/pubmed/36983097
http://dx.doi.org/10.3390/jcm12062094
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