Diagnostic efficiency of deep and surface knowledge in KARDIO

摘要

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 two 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 ‘compiled’ into surface diagnostic rules. Through simulation, a relational table is generated and subsequently compressed into efficient diagnostic rules by inductive learning. A novel contribution to KARDIO, presented here, includes a comparison of diagnostic efficiency and space complexity of four types of knowledge: a simulation model of the heart, a hierarchical four-level model, a relational table, and compressed diagnostic rules.