Crafting knowledge-based systems: expert systems made realistic
Crafting knowledge-based systems: expert systems made realistic
Stochastic Petri net modeling of wave sequences in cardiac arrhythmias
Computers and Biomedical Research
Computer model of cardiac repolarization processes and of the recover sequence
Computers and Biomedical Research
Artificial intelligence, simulation, and modeling: a critical survey
Artificial intelligence, simulation & modeling
Semi-quantitative “close enough” systems dynamics models: an alternative to qualitative simulation
Artificial intelligence, simulation & modeling
KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
Innovative Applications of Artificial Intelligence
Innovative Applications of Artificial Intelligence
Expert Systems for Monitoring and Control
Expert Systems for Monitoring and Control
On using causal knowledge to recognize vital signals: a study of knowledge-based interpretation of arrythmias
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
The limits of qualitative simulation
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Book review: KARDIO: A study in deep and qualitative knowledge for expert systems
Artificial Intelligence in Medicine
Flexible reasoning about patient management using multiple models
Artificial Intelligence in Medicine
Using quantitative and qualitative constraints in models of cardiac electrophysiology
Artificial Intelligence in Medicine
Editorial: Intelligent monitoring and control of dynamic physiological systems
Artificial Intelligence in Medicine
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The cardiac arrhythmias are abnormalities of the heart beat as observed on the electrocardiogram. They are interesting to workers in medical knowledge-based systems because their full interpretation requires anatomical and temporal reasoning using causal models. This paper presents an architecture for a knowledge-based cardiac arrhythmia monitor in which model-based hypotheses are built dynamically to explain the current portion of a continuous electrocardiographic signal. Based on the hypothesize-and-test paradigm, the control algorithm selects members of a hierarchy of electrophysiologic models and adapts them to the current rhythm to form the current hypotheses. Expectations are generated for each of the current hypotheses and compared with the incoming signal. Mismatched hypotheses are discarded except in special cases relating to artifact or changes in underlying rhythm type. A prototype has been implemented to test the basic concepts of this architecture.