Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment

Abstract Hypothesis Diabetes Type 1 (DT1) therapy by means of artificial pancreas consisting of insulin pump with continuous glucose monitoring and hybrid "closed-loop" control algorithm trained with machine learning (ML) technology provides better glycemia control than multi-daily injecti...

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Autor: Peiro, Joan Carles
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Ramon Llull (URL)
Repositorio:DAU Arxiu Digital de la Universitat Ramon Llull
OAI Identifier:oai:dau.url.edu:20.500.14342/5369
Acceso en línea:http://hdl.handle.net/20.500.14342/5369
Access Level:acceso abierto
Palabra clave:Artificial Pancreas
Closed-loop
Hybrid Closed-loop
Automatic Insulin Delivery
Machine Learning
Continous Glucose Monitor
Sensor Assited Insulin Pump
004
61
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oai_identifier_str oai:dau.url.edu:20.500.14342/5369
network_acronym_str ES
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spelling Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes TreatmentPeiro, Joan CarlesArtificial PancreasClosed-loopHybrid Closed-loopAutomatic Insulin DeliveryMachine LearningContinous Glucose MonitorSensor Assited Insulin Pump00461Abstract Hypothesis Diabetes Type 1 (DT1) therapy by means of artificial pancreas consisting of insulin pump with continuous glucose monitoring and hybrid "closed-loop" control algorithm trained with machine learning (ML) technology provides better glycemia control than multi-daily injection, insulin pump without continuous glucose monitoring (CGM) and sensor assisted insulin pump therapies. Methods Using Accu-Chek smart pix software to analyze the data collected in-vivo by JC Peiró, author and DT1 patient, in the period August 2004 to August 2019. AccuChek smart pix has been used to collect 4.621 glycemia tests for a period of 1.241 days. The period measured with continuous glucose monitoring contains data with +90% sensor coverage. Control graphics measure mean and median glycemia, standard deviation, time in range, time above range, time below range, hypoglycemia periods, high blood glucose index and low blood glucose index. In-vivo analysis is validated in-silico using the UVA/Padova T1DMS simulator on a population of 30 individuals of different ages under multi-daily Injection and under "closed-loop" artificial pancreas therapies. Results Compared to multi daily injection (MDI) therapy, the new artificial pancreas with hybrid "closed-loop" machine learning trained control algorithm reduces 70.7% the periods above range, reduces 67.2% periods below range, time in range% increases 75%, hypoglycemia periods reduce 91.2%, high blood glycemic index reduces 67%, low blood glycemic index reduces 73.8%, median glycemia reduces 20% and glycated hemoglobin reduces 20.1%. Conclusions/interpretation Therapy for diabetes type 1 using "hybrid closed-loop" artificial pancreas controlled by machine learning trained algorithm provides better glycemic control results than the analyzed therapies of multi daily injection, insulin pump without glucose monitoring and "sensor assisted" insulin pumpinfo:eu-repo/semantics/publishedVersionUniversitat Ramon Llull. La Salle2025202520202021info:eu-repo/semantics/article6 p.application/pdfhttp://hdl.handle.net/20.500.14342/5369reponame:DAU Arxiu Digital de la Universitat Ramon Llullinstname:Universitat Ramon Llull (URL)InglésManchester Journal of Artificial Intelligence & Applied Sciences. Vol. 2 Nº. 1 (2021)© Manchester Journal of Artificial Intelligence & Applied Sciences. Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:dau.url.edu:20.500.14342/53692026-06-21T06:40:37Z
dc.title.none.fl_str_mv Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
title Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
spellingShingle Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
Peiro, Joan Carles
Artificial Pancreas
Closed-loop
Hybrid Closed-loop
Automatic Insulin Delivery
Machine Learning
Continous Glucose Monitor
Sensor Assited Insulin Pump
004
61
title_short Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
title_full Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
title_fullStr Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
title_full_unstemmed Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
title_sort Automatic Insulin Delivery: Artificial Pancreas Controlled by Machine Learning Trained Algorithm Compared to other Therapies for Diabetes Treatment
dc.creator.none.fl_str_mv Peiro, Joan Carles
author Peiro, Joan Carles
author_facet Peiro, Joan Carles
author_role author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. La Salle
dc.subject.none.fl_str_mv Artificial Pancreas
Closed-loop
Hybrid Closed-loop
Automatic Insulin Delivery
Machine Learning
Continous Glucose Monitor
Sensor Assited Insulin Pump
004
61
topic Artificial Pancreas
Closed-loop
Hybrid Closed-loop
Automatic Insulin Delivery
Machine Learning
Continous Glucose Monitor
Sensor Assited Insulin Pump
004
61
description Abstract Hypothesis Diabetes Type 1 (DT1) therapy by means of artificial pancreas consisting of insulin pump with continuous glucose monitoring and hybrid "closed-loop" control algorithm trained with machine learning (ML) technology provides better glycemia control than multi-daily injection, insulin pump without continuous glucose monitoring (CGM) and sensor assisted insulin pump therapies. Methods Using Accu-Chek smart pix software to analyze the data collected in-vivo by JC Peiró, author and DT1 patient, in the period August 2004 to August 2019. AccuChek smart pix has been used to collect 4.621 glycemia tests for a period of 1.241 days. The period measured with continuous glucose monitoring contains data with +90% sensor coverage. Control graphics measure mean and median glycemia, standard deviation, time in range, time above range, time below range, hypoglycemia periods, high blood glucose index and low blood glucose index. In-vivo analysis is validated in-silico using the UVA/Padova T1DMS simulator on a population of 30 individuals of different ages under multi-daily Injection and under "closed-loop" artificial pancreas therapies. Results Compared to multi daily injection (MDI) therapy, the new artificial pancreas with hybrid "closed-loop" machine learning trained control algorithm reduces 70.7% the periods above range, reduces 67.2% periods below range, time in range% increases 75%, hypoglycemia periods reduce 91.2%, high blood glycemic index reduces 67%, low blood glycemic index reduces 73.8%, median glycemia reduces 20% and glycated hemoglobin reduces 20.1%. Conclusions/interpretation Therapy for diabetes type 1 using "hybrid closed-loop" artificial pancreas controlled by machine learning trained algorithm provides better glycemic control results than the analyzed therapies of multi daily injection, insulin pump without glucose monitoring and "sensor assisted" insulin pump
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/5369
url http://hdl.handle.net/20.500.14342/5369
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Manchester Journal of Artificial Intelligence & Applied Sciences. Vol. 2 Nº. 1 (2021)
dc.rights.none.fl_str_mv © Manchester Journal of Artificial Intelligence & Applied Sciences. Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Manchester Journal of Artificial Intelligence & Applied Sciences. Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 6 p.
application/pdf
dc.source.none.fl_str_mv reponame:DAU Arxiu Digital de la Universitat Ramon Llull
instname:Universitat Ramon Llull (URL)
instname_str Universitat Ramon Llull (URL)
reponame_str DAU Arxiu Digital de la Universitat Ramon Llull
collection DAU Arxiu Digital de la Universitat Ramon Llull
repository.name.fl_str_mv
repository.mail.fl_str_mv
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