title: Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor.
abstract: BACKGROUND
  Physicians tailor insulin dosing based on blood glucose goals, response to insulin, compliance, lifestyle, eating habits, daily schedule, and fear of and ability to detect hypoglycemia.
  
  
  METHODS
  We introduce a method that allows a physician to tune a fuzzy logic controller (FLC) artificial pancreas (AP) for a particular patient. It utilizes the physician's judgment and weighing of various factors. The personalization factor (PF) is a scaling of the dose produced by the FLC and is used to customize the dosing. The PF has discrete values of 1 through 5. The proposed method was developed using a database of results from 30 University of Virginia/Padova Metabolic Simulator in silico subjects (10 adults, 10 adolescents, and 10 children). Various meal sizes and timing were used to provide the physician information on which to base an initial dosing regimen and PF. Future decisions on dosing aggressiveness using the PF would be based on the patient's data at follow-up.
  
  
  RESULTS
  Three examples of a wide variation in diabetes situations are given to illustrate the physician's thought process when initially configuring the AP system for a specific patient.
  
  
  CONCLUSIONS
  Fuzzy logic controllers are developed by encoding human expertise into the design of the controller. The FLC methodology allows for the real-time scaling of doses without compromising the integrity of the dosing rules matrix. The use of the PF to individualize the AP system is enabled by the fuzzy logic development methodology.
authors.affiliation: Division of Pediatric Endocrinology, Department of Pediatrics, University of Washington, Seattle, Washington, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Mauseth
authors.forename: Youqing
authors.initials: Y
authors.lastname: Wang
authors.forename: Eyal
authors.initials: E
authors.lastname: Dassau
authors.forename: Robert
authors.initials: R
authors.lastname: Kircher
authors.forename: Donald
authors.initials: D
authors.lastname: Matheson
authors.forename: Howard
authors.initials: H
authors.lastname: Zisser
authors.forename: Lois
authors.initials: L
authors.lastname: Jovanovic
authors.forename: Francis J
authors.initials: FJ
authors.lastname: Doyle
mesh_terms: D000293:Adolescent; D000328:Adult; D000375:Aging / Q000502:physiology; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis / Q000378:metabolism; D002675:Child, Preschool; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000178:diet therapy / Q000188:drug therapy*; D003927:Diet, Diabetic; D004040:Dietary Carbohydrates / Q000097:blood; D005260:Female; D017143:Fuzzy Logic; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000188:drug therapy; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use; D008297:Male; D019397:Pancreas, Artificial; D010820:Physicians; D057285:Precision Medicine
chemical_list: D001786:Blood Glucose; D004040:Dietary Carbohydrates; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin