Tools

LEADER 03883cam 2200505 i 4500

001 on1450451726;

003 OCoLC;

005 20260527050255.0;

006 m o d ;

007 cr unu||||||||;

008 240731s2024 ncua obm 000 0 eng d;

035 a| (OCoLC)1450451726;

090 a| Q325.6;

049 a| EREE;

100 1 a| Ellis, Zackarie,e| author.;

245 10 a| Application of reinforcement learning algorithm to minimize the dosage of insulin infusion /c| by Zackarie Ellis.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2024.;

300 a| 1 online resource (59 pages) :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 3.61 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2024.;

500 a| Presented to the Faculty of the Department of Engineering;

500 a| Advisor: Jinkun Lee;

500 a| Title from PDF t.p. (viewed July 9, 2025).;

520 3 a| The control of insulin infusion pumps has been receiving attention from various sectors since artificial intelligence (AI) has been successfully applied to many optimal control problems. Data-driven control has become more popular with help of AI without dynamic model structure, but a white-box control would be more efficient if there exist an identified model structure. In the case of insulin pump control design, traditional mathematical glucose-insulin models will be very useful to find an optimal insulin infusion profile by applying AI technique. The objective of insulin pump control in this study is to minimize the total amount of insulin dosage while maintaining the plasma glucose concentration level within a healthy range by controlling the profile of insulin infusion rate. Reinforcement learning is used to address this dynamic glucose-insulin interaction problem with a goal of achieving minimum insulin dosage. Reinforcement learning in this study finds an optimal insulin infusion profile with minimum dosage that stabilizes a plasma glucose concentration from an initial hyperglycemic state to a basal level in a reasonable amount of time. Since diabetes is known as a chronic disease, minimal insulin dosage control may be important to decrease potential insulin resistance and play an important role in the long-term treatment of diabetes patients. An insulin infusion profile will next be generated in the presence of a meal disturbance to understand the ability of a proposed reinforcement learning algorithm to be insensitive to disturbances in glucose and act as a normal human pancreas. Numerical examples are provided of glucose-insulin dynamics models, and the proposed insulin infusion profiles are verified by comparing them with two optimal insulin infusion programs suggested by Fisher's study and a normal physiological plasma insulin response to a meal, respectively.;

504 a| Includes bibliographical references.;

650 0 a| Reinforcement learning.;

650 0 a| Insulin pumpsx| Design.;

650 0 a| Algorithms.;

650 0 a| Diabetesx| Treatment.;

650 7 a| algorithms.2| aat0| (CStmoGRI)aat300065585;

653 a| Glucose-Insulin Dynamic Model;

700 1 a| Lee, Jinkun,e| degree supervisor.;

710 2 a| East Carolina University.b| Department of Engineering.;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/13466;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 6462766;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462766-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 7/31/2024x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462766-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 7/31/2024x| ETDz| HERESOURCE;

Application of reinforcement learning algorithm to minimize the dosage of insulin infusion / by Zackarie Ellis.

Author/creator	Ellis, Zackarie author.
Other author	Lee, Jinkun, degree supervisor.
Other author	East Carolina University. Department of Engineering.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2024.
Description	1 online resource (59 pages) : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	Reinforcement learning. Insulin pumps--Design. Algorithms. Diabetes--Treatment.

Summary	The control of insulin infusion pumps has been receiving attention from various sectors since artificial intelligence (AI) has been successfully applied to many optimal control problems. Data-driven control has become more popular with help of AI without dynamic model structure, but a white-box control would be more efficient if there exist an identified model structure. In the case of insulin pump control design, traditional mathematical glucose-insulin models will be very useful to find an optimal insulin infusion profile by applying AI technique. The objective of insulin pump control in this study is to minimize the total amount of insulin dosage while maintaining the plasma glucose concentration level within a healthy range by controlling the profile of insulin infusion rate. Reinforcement learning is used to address this dynamic glucose-insulin interaction problem with a goal of achieving minimum insulin dosage. Reinforcement learning in this study finds an optimal insulin infusion profile with minimum dosage that stabilizes a plasma glucose concentration from an initial hyperglycemic state to a basal level in a reasonable amount of time. Since diabetes is known as a chronic disease, minimal insulin dosage control may be important to decrease potential insulin resistance and play an important role in the long-term treatment of diabetes patients. An insulin infusion profile will next be generated in the presence of a meal disturbance to understand the ability of a proposed reinforcement learning algorithm to be insensitive to disturbances in glucose and act as a normal human pancreas. Numerical examples are provided of glucose-insulin dynamics models, and the proposed insulin infusion profiles are verified by comparing them with two optimal insulin infusion programs suggested by Fisher's study and a normal physiological plasma insulin response to a meal, respectively.
General note	Presented to the Faculty of the Department of Engineering
General note	Advisor: Jinkun Lee
General note	Title from PDF t.p. (viewed July 9, 2025).
Dissertation note	M.S. East Carolina University 2024.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

Application of reinforcement learning algorithm to minimize the dosage of insulin infusion / by Zackarie Ellis.

Click here for more information about this title

Availability