KARDIO: a study in deep and qualitative knowledge for expert systems

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This book is the first detailed account of the development of a complex and successful expert system based on deep and qualitative knowledge. It shows how the qualitative modeling approach, using logic based representations and machine learning techniques, can be used to construct knowledge bases whose complexity is far beyond the capability of traditional, dialogue based techniques of knowledge acquisition.The relevant techniques are demonstrated in full detail in the building of Kardio, a medical expert system model of the human heart designed for the diagnosis of cardiac arrhythmias. Kardio's performance is estimated by cardiologists to be equivalent to that of a specialist of internal medicine (not a cardiologist) who is highly skilled in the reading of ECG recordings, and it can be used as a diagnostic tool in ECG interpretation. It may also be used for instruction in electrocardiography.The authors show how the model was compiled, by means of qualitative simulation and machine learning tools, into various representations that are suited for particular expert tasks. They investigate a hierarchical organization of a qualitative model and outline an experiment whereby the construction of a deep model is automated by means of machine learning techniques. The book contains a complete model of the electrical system of the heart that can be used to further development in this area of applications.Ivan Bratko, author of Prolog Programming for Artificial Intelligence, is a professor of computer science at E. Kardelj University and leads the AI laboratory at the Jozef Stefan Institute in Ljubljana, Yugoslavia. Igor Mozetic and Nada Lavrac are researchers at the institute.

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Qualitative Model of the Heart
Model Interpretation and Derivation

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This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003.

The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.

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