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Model-Free Intelligent Diabetes Management Using Machine Learning

  • Author / Creator
    Bastani, Meysam
  • 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.

  • Subjects / Keywords
  • Graduation date
    2014-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R34Q7QX0S
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. 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.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Greiner, Russell (Computing Science)
  • Examining committee members and their departments
    • Bowling, Michael (Computing Science)
    • Ryan, Edmond A. (Medicine)
    • Wishart, David (Computing Science)