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High level control strategies for diabetes therapy

  • Alberto Riva
  • Riccardo Bellazzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)

Abstract

The project we describe here is aimed at assisting out-patients affected by Insulin Dependent Diabetes Mellitus. Our approach exploits the usual scheme of diabetic patients management, based on (i) a periodic evaluation of the patients' metabolic control performed by the physician, and (ii) patient-tailored tables for self-adjustments of insulin dosages. Following this scheme we have defined a system built on a two-levels architecture, that can be conveniently implemented in a telemedicine context. The High Level Module exploits both medical knowledge and clinical information in order to assess an insulin protocol, defined in terms of insulin timing, type, and total amount. The High Level Module exchanges information with the Low Level Module in order to define the control actions to be taken at the low level, as well as to periodically evaluate protocol adequacy on the basis of patient data. The goal of the Low Level Module, whose characteristics can be chosen by the High-Level Module, is to suggest the next insulin dosage, depending on the actual blood glucose measurement and a certain pre-defined insulin delivery protocol. In this paper we outline the overall organization of the system and we describe in detail the methodology and the strategies exploited by the high-level module.

Keywords

Insulin Dosage Insulin Injection Regular Insulin High Level Module Insulin Dosage Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    S. Andreassen, J. Benn, R. Hovorka, K.G. Olesen, E.R. Carson, A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study, Computer Methods and Programs in Biomedicine, 41 (1994) 153–165.PubMedGoogle Scholar
  2. 2.
    R. Bellazzi, C. Siviero, M. Stefanelli, G. De Nicolao Adaptive controllers for intelligent monitoring To appear in: Artificial Intelligence in Medicine Journal.Google Scholar
  3. 3.
    M. P. Berger, R. A. Gelfand, P.L. Miller, Combining statistical, rule-based and physiologic model-based methods to assist in the management of diabetes mellitus, Computers and Biomedical Research, 23 (1990) 346–357.PubMedGoogle Scholar
  4. 4.
    J. Beyer, J. Schrezenmeir, G. Schulz, T. Strack, E. Kustner, G. Shulz, The influence of different generations of computer algorithms on diabetes control, Computer Methods and Programs in Biomedicine, 32 (1990) 225–232.PubMedGoogle Scholar
  5. 5.
    T. Deutsch, E.D. Lehmann, E.R. Carson, A.V. Roudsari, K.D. Hopkins, P.H. Sönksen Time series analysis and control of blood glucose levels in diabetic patients Computer Methods and Programs in Biomedicine, 41 (1994) 167–182.PubMedGoogle Scholar
  6. 6.
    T. Hauser, L.V. Campbell, E.W. Kraegen and D.J. Chisholm, Glycaemic response to an insulin dose change: computer simulator predictions vs mean patient responses, Diabetes Nutrition and Metabolism, 7 (1994) 89–95.Google Scholar
  7. 7.
    E.D. Lehmann, T. Deutsch, E.R. Carson, P.H. Sönksen, AIDA: an interactive diabetes advisor, Computer Methods and Programs in Biomedicine, 41 (1994) 184–203.CrossRefGoogle Scholar
  8. 8.
    M.Ramoni, A. Riva, M. Stefanelli, V. Patel, Forecasting glucose concentration in diabetic patients using ignorant belief networks, Proceedings of the AAAI Spring Symposium on Artificial Intelligence in Medicine, Stanford, CA, 1994.Google Scholar
  9. 9.
    E. Salzsieder, G. Albrecht, E. Jutzi, U. Fischer, Estimation of individual adapted control parameters for an artificial beta-cell, Biomed. Biochem. Acta, 43 (1984) 585–596.Google Scholar
  10. 10.
    A. Schiffrin, M. Mihic, B.S. Leibel, A.M. Albisser, Computer Assisted Insulin Dosage Adjustment, Diabetes Care, 8 (1985) 545–552.PubMedGoogle Scholar
  11. 11.
    J.Schlichtkrull, O. Munck, M. Jersild, The M-value, an index of blood sugar control in diabetics, Acta Med. Scand. 177, 95–102.Google Scholar
  12. 12.
    D. Spiegelhalter, A. Dawid, S. Lauritzen, R. Cowell, Bayesian Analysis in Expert Systems. Statistical Science, 8 (1993) 219–283.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Alberto Riva
    • 1
  • Riccardo Bellazzi
    • 1
  1. 1.Laboratorio di Informatica Medica Dipartimento di Informatica e SistemisticaUniversità di PaviaPaviaItaly

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