Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Case Reports
. 2010 Jul 1;4(4):913-22.
doi: 10.1177/193229681000400422.

Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor

Richard Mauseth  1 , Youqing Wang, Eyal Dassau, Robert Kircher Jr, Donald Matheson, Howard Zisser, Lois Jovanovic, Francis J Doyle 3rd
Affiliations
Case Reports

Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor

Richard Mauseth et al. J Diabetes Sci Technol. .

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.

Method: 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.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Fuzzy logic as a black box mapping of an input space to an output.
Figure 2.
Figure 2.
Notional data flow diagram for FL process. BGL, blood glucose level; VH, very high; H, high; M, medium; L, low; VL, very low; VN, very negative; N, negative; Z, zero; P, positive; VP, very positive.
Figure 3.
Figure 3.
Correlation of points on a BG trajectory with sets of rules in the FL dosing matrix. BGL, blood glucose level; VN, very negative; N, negative; Z, zero; P, positive; VP, very positive.
Figure 4.
Figure 4.
Five-minute dosing matrix. BGL, blood glucose level; VN, very negative; N, negative; Z, zero; P, positive; VP, very positive.
Figure 5.
Figure 5.
Results of four subject clinical trials using initial FLC.
Figure 6.
Figure 6.
The system level diagram for the FLC AP controller. CGM, continuous glucose monitor.
Figure 7.
Figure 7.
Mean BG, LBGI, and HBGI for 30 in silico adult subjects for standard day.
Figure 8.
Figure 8.
Mean BG, LBGI, and HBGI for 30 in silico adult subjects in fasting period.
Figure 9.
Figure 9.
Mean BG for 30 in silico adult subjects, PF 1–5, range of meal sizes.
Figure 10.
Figure 10.
LBGI for 30 in silico adult subjects, PF 1–5, range of meal sizes.
Figure 11.
Figure 11.
Mean BG, LBGI, and HBGI for 30 in silico child subjects for standard day.
Figure 12.
Figure 12.
Mean BG, LBGI, and HBGI for 30 in silico child subjects in fasting period.
Figure 13.
Figure 13.
Mean BG for 30 in silico child subjects, PF 1–5, range of meal sizes.
Figure 14.
Figure 14.
Low blood glucose index for 30 in silico child subjects, PF 1–5, range of meal sizes.
Figure 15.
Figure 15.
Mean BG, LBGI, and HBGI for 30 in silico adolescent subjects for standard day.
Figure 16.
Figure 16.
Mean BG, LBGI, and HBGI for 30 in silico adolescent subjects in fasting period.
Figure 17.
Figure 17.
Mean BG for 30 in silico adolescent subjects, PF 1–5, range of meal sizes.
Figure 18.
Figure 18.
Low blood glucose index for 30 in silico adolescent subjects, PF 1–5, range of meal sizes.
See this image and copyright information in PMC

References

    1. Diabetes Control and Complications Trial Research Group The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New Engl J Med. 1993;329(14):977–86. - PubMed
    1. Juvenile Diabetes Research Foundation Artificial Pancreas Project http://www.jdrf.org/index.cfm?page_id=104576. - PMC - PubMed
    1. Zadeh LA. Fuzzy sets. Inform Control. 1965;8:338–353.
    1. The MathWorks Fuzzy inference systems. http://www.mathworks.com/access/helpdesk/help/toolbox/fuzzy/fp351dup8.html.
    1. Mamdani EH. Application of fuzzy algorithms for control of simple dynamic plant. Proc IEE. 1974;121(12):1585–1588.

Publication types

MeSH terms