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Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas

Objective: A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose app...

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Main Authors: Davari Benam, Karim, Gros, Sebastien Nicolas, Fougner, Anders Lyngvi
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creator Davari Benam, Karim
Gros, Sebastien Nicolas
Fougner, Anders Lyngvi
description Objective: A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. Method: The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. Results: The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. Conclusion: The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. Significance: This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.
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This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. Method: The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. Results: The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. Conclusion: The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. Significance: This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.</description><language>eng</language><publisher>IEEE</publisher><subject>Artificial Pancreas ; Automatisk glukoseregulering ; Closed loop glucose control ; Intraperitoneal insulin infusion ; Intraperitoneal insulininfusjon ; Kunstig bukspyttkjertel ; Type 1 diabetes</subject><creationdate>2023</creationdate><rights>info:eu-repo/semantics/openAccess</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,780,885,26567</link.rule.ids><linktorsrc>$$Uhttp://hdl.handle.net/11250/3086095$$EView_record_in_NORA$$FView_record_in_$$GNORA$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Davari Benam, Karim</creatorcontrib><creatorcontrib>Gros, Sebastien Nicolas</creatorcontrib><creatorcontrib>Fougner, Anders Lyngvi</creatorcontrib><title>Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas</title><description>Objective: A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. Method: The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. Results: The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. Conclusion: The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. Significance: This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.</description><subject>Artificial Pancreas</subject><subject>Automatisk glukoseregulering</subject><subject>Closed loop glucose control</subject><subject>Intraperitoneal insulin infusion</subject><subject>Intraperitoneal insulininfusjon</subject><subject>Kunstig bukspyttkjertel</subject><subject>Type 1 diabetes</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>3HK</sourceid><recordid>eNqNTrsKwkAQTGMh6j-sHxCIhoiWISZGsAiidVguG1i43IbNpbD3wz3ED7CaBzPDLKN3OXke0LM4QNdBo9Sx-Urp4WJnIxNBPo6Eis4Q3NET9KLwDD6HElSztS8obAh28U1khPOMNq5FB3EEV-cVR1L2QaGFXD33bDjQJiwq4bSOFj3aiTY_XEXbqnwUdWyUwz3XOlFsd7t9lrRpcjwkpyz9J_MBv-tJUw</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Davari Benam, Karim</creator><creator>Gros, Sebastien Nicolas</creator><creator>Fougner, Anders Lyngvi</creator><general>IEEE</general><scope>3HK</scope></search><sort><creationdate>2023</creationdate><title>Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas</title><author>Davari Benam, Karim ; Gros, Sebastien Nicolas ; Fougner, Anders Lyngvi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-cristin_nora_11250_30860953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Pancreas</topic><topic>Automatisk glukoseregulering</topic><topic>Closed loop glucose control</topic><topic>Intraperitoneal insulin infusion</topic><topic>Intraperitoneal insulininfusjon</topic><topic>Kunstig bukspyttkjertel</topic><topic>Type 1 diabetes</topic><toplevel>online_resources</toplevel><creatorcontrib>Davari Benam, Karim</creatorcontrib><creatorcontrib>Gros, Sebastien Nicolas</creatorcontrib><creatorcontrib>Fougner, Anders Lyngvi</creatorcontrib><collection>NORA - Norwegian Open Research Archives</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Davari Benam, Karim</au><au>Gros, Sebastien Nicolas</au><au>Fougner, Anders Lyngvi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas</atitle><date>2023</date><risdate>2023</risdate><abstract>Objective: A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. Method: The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. Results: The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. Conclusion: The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. Significance: This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.</abstract><pub>IEEE</pub><oa>free_for_read</oa></addata></record>
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source NORA - Norwegian Open Research Archives
subjects Artificial Pancreas
Automatisk glukoseregulering
Closed loop glucose control
Intraperitoneal insulin infusion
Intraperitoneal insulininfusjon
Kunstig bukspyttkjertel
Type 1 diabetes
title Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas
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