High level control strategies for diabetes therapy
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.
KeywordsInsulin Dosage Insulin Injection Regular Insulin High Level Module Insulin Dosage Adjustment
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- 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
- 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
- 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.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
- 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.D. Spiegelhalter, A. Dawid, S. Lauritzen, R. Cowell, Bayesian Analysis in Expert Systems. Statistical Science, 8 (1993) 219–283.Google Scholar