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Permanent link (DOI): https://doi.org/10.7939/R34Q7QX0S

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Model-Free Intelligent Diabetes Management Using Machine Learning Open Access

Descriptions

Other title
Subject/Keyword
reinforcement learning
policy gradient
diabetes
insulin dosage adjustment
supervised learning
machine learning
type-1 diabetes
actor-critic
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Bastani, Meysam
Supervisor and department
Greiner, Russell (Computing Science)
Examining committee member and department
Bowling, Michael (Computing Science)
Ryan, Edmond A. (Medicine)
Wishart, David (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2013-11-22T11:30:37Z
Graduation date
2014-06
Degree
Master of Science
Degree level
Master's
Abstract
Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual injection dose is based on a formula that takes current blood glucose level and the meal size into consideration. While following this insulin regimen, the patient records their insulin injections, blood glucose readings, meal sizes and potentially other information in a diabetes diary. During clinical visits, the diabetologist analyzes these records to decide how best to adjust the patient's insulin formula. This research provides methods from supervised learning and reinforcement learning that automatically adjust this formula using data from a patient's diabetes diary. These methods are evaluated on twenty \emph{in-silico} patients, achieving a performance that is often comparable to that of an expert diabetologist. Our experimental results demonstrate that both supervised learning and reinforcement learning methods appear effective in helping to manage diabetes.
Language
English
DOI
doi:10.7939/R34Q7QX0S
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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Last modified: 2016:08:04 01:25:00-06:00
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File title: Model-Free Intelligent Diabetes Management Using Machine Learning
File author: Meysam Bastani
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