Literature DB >> 15000368

Expert PID control system for blood glucose control in critically ill patients.

Frederick Chee1, Tyrone L Fernando, Andrey V Savkin, Vernon van Heeden.   

Abstract

In this paper, the design of an expert proportional-integral-derivative (PID) control system for blood glucose regulation in patients, such as the intensive care population, is described. The control system applied the concept of expert system, and proportional, integral and derivative control in clinical sliding table technique, to effect a control algorithm in the form of an "active" sliding table that is used to prescribe insulin infusion rates. This combination provided dynamic properties to the sliding table control. Clinical results have shown a comparable performance between the control system and routine clinical treatment, in terms of blood glucose level maintained. Nevertheless, the control system is sensitive to sensor reading artefact, particularly in the lower ranges of blood glucose level, mandating manual intervention.

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Year:  2003        PMID: 15000368     DOI: 10.1109/titb.2003.821326

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  24 in total

1.  A switching control strategy for the attenuation of blood glucose disturbances.

Authors:  Mihalis G Markakis; Georgios D Mitsis; George P Papavassilopoulos; Petros A Ioannou; Vasilis Z Marmarelis
Journal:  Optim Control Appl Methods       Date:  2011       Impact factor: 2.530

Review 2.  Essential elements of the native glucoregulatory system, which, if appreciated, may help improve the function of glucose controllers in the intensive care unit setting.

Authors:  Leon DeJournett
Journal:  J Diabetes Sci Technol       Date:  2010-01-01

3.  Glycemia prediction in critically ill patients using an adaptive modeling approach.

Authors:  Tom Van Herpe; Marcelo Espinoza; Niels Haverbeke; Bart De Moor; Greet Van den Berghe
Journal:  J Diabetes Sci Technol       Date:  2007-05

4.  Pilot study of the SPRINT glycemic control protocol in a Hungarian medical intensive care unit.

Authors:  Balazs Benyo; Attila Illyés; Noémi Szabó Némedi; Aaron J Le Compte; Attila Havas; Levente Kovacs; Liam Fisk; Geoffrey M Shaw; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2012-11-01

5.  A novel adaptive-weighted-average framework for blood glucose prediction.

Authors:  Youqing Wang; Xiangwei Wu; Xue Mo
Journal:  Diabetes Technol Ther       Date:  2013-07-24       Impact factor: 6.118

6.  Continuous glucose monitors and the burden of tight glycemic control in critical care: can they cure the time cost?

Authors:  Matthew Signal; Christopher G Pretty; J Geoffrey Chase; Aaron Le Compte; Geoffrey M Shaw
Journal:  J Diabetes Sci Technol       Date:  2010-05-01

7.  Overview of glycemic control in critical care: relating performance and clinical results.

Authors:  J Geoffrey Chase; Christopher E Hann; Geoffrey M Shaw; Jason Wong; Jessica Lin; Thomas Lotz; Aaron Lecompte; Timothy Lonergan
Journal:  J Diabetes Sci Technol       Date:  2007-01

8.  The artificial pancreas: how sweet engineering will solve bitter problems.

Authors:  David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2007-01

9.  A benchmark data set for model-based glycemic control in critical care.

Authors:  J Geoffrey Chase; Aaron LeCompte; Geoffrey M Shaw; Amy Blakemore; Jason Wong; Jessica Lin; Christopher E Hann
Journal:  J Diabetes Sci Technol       Date:  2008-07

10.  A Feedforward-Feedback Glucose Control Strategy for Type 1 Diabetes Mellitus.

Authors:  Gianni Marchetti; Massimiliano Barolo; Lois Jovanovič; Howard Zisser; Dale E Seborg
Journal:  J Process Control       Date:  2008-02       Impact factor: 3.666

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