Model-Based Biosignal Interpretation

S. Andreassen

doi:10.1055/s-0038-1634976

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1994; 33(01): 103-110
DOI: 10.1055/s-0038-1634976

Original Article

Schattauer GmbH

Model-Based Biosignal Interpretation

S. Andreassen

¹Department of Medical Informatics and Image Analysis, Institute for Electronic Systems, Aalborg University, Denmark

› Author Affiliations

Abstract

Abstract:

Two relatively new approaches to model-based biosignal interpretation, qualitative simulation and modelling by causal probabilistic networks, are compared to modelling by differential equations. A major problem in applying a model to an individual patient is the estimation of the parameters. The available observations are unlikely to allow a proper estimation of the parameters, and even if they do, the task appears to have exponential computational complexity if the model is non-linear. Causal probabilistic networks have both differential equation models and qualitative simulation as special cases, and they can provide both Bayesian and maximum-likelihood parameter estimates, in most cases in much less than exponential time. In addition, they can calculate the probabilities required for a decision-theoretical approach to medical decision support. The practical applicability of causal probabilistic networks to real medical problems is illustrated by a model of glucose metabolism which is used to adjust insulin therapy in type I diabetic patients.

Keywords

Model-based Biosignal Interpretation - Parameter Estimation - Causal Probabilistic Networks - Simulation - Physiological Systems

Full Text

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