title: Model-based biosignal interpretation.
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.
authors.affiliation: Department of Medical Informatics and Image Analysis, Aalborg University, Denmark.
authors.forename: S
authors.initials: S
authors.lastname: Andreassen
mesh_terms: D001499:Bayes Theorem; D001786:Blood Glucose / Q000378:metabolism; D003920:Diabetes Mellitus / Q000378:metabolism; D004562:Electrocardiography; D004566:Electrodes; D006801:Humans; D016018:Least-Squares Analysis; D008954:Models, Biological; D008991:Monitoring, Physiologic; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes
chemical_list: D001786:Blood Glucose