Improving Diagnostic Efficiency in KARDIO: Abstractions, Constraint Propagation, and Model Compilation

ions, Constraint Propagation, and Model Compilation 1 Igor Mozetic and Bernhard Pfahringer Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna, Austria e-mail: (igor,bernhard)@ai.univie.ac.at Abstract The KARDIO system deals with the problem of diagnosing cardiac arrhythmias from symbolic descriptions of electrocardiograms. The system incorporates a qualitative model which simulates the electrical activity of the heart. In the paper we outline three methods for an efficient application of a simulation model to diagnosis. First, through abstractions and refinements, the model is represented at several levels of detail. Second, the model is reformulated in terms of constraints which enable efficient propagation of relational dependencies and reduce backtracking. And finally, the model is `compiled' into surface diagnostic rules. Through simulation, a relational table is generated and subsequently compressed into efficient diagnostic rules by inductive..