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Evaluation of a Guided Machine Learning Approach for Pharmacokinetic Modeling


Abstract A medical control system, a real-time controller, uses a predictive model of human physiology for estimation and controlling of drug concentration in the human body. Artificial Pancreas (AP) is an example of the control system which regulates blood glucose in T1D patients. The predictive model in the control system such as Bergman Minimal Model (BMM) is based on physiological modeling technique which separates the body into the number of anatomical compartments and each compartment's effect on body system is determined by their physiological parameters. These models are less accurate due to unaccounted physiological factors effecting target values. Estimation of a large number of physiological parameters through optimization algorithm ... (more)
Created Date 2017
Contributor Agrawal, Anurag (Author) / Gupta, Sandeep K. S. (Advisor) / Banerjee, Ayan (Committee member) / Kudva, Yogish (Committee member) / Arizona State University (Publisher)
Subject Computer science / Clinical psychology / Artificial Pancreas / Blood Glucose / Machine Learning / Physical Activity / Physiological Model / Support Vector Regression
Type Masters Thesis
Extent 46 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis