Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Diagnostic Rules of Increased Reliability for Critical Medical Applications
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Combining Unsupervised and Supervised Machine Learning
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Run-time validation of knowledge-based systems
Proceedings of the seventh international conference on Knowledge capture
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The work presents the results of inconsistency detection experiments on the data records of an atherosclerotic coronary heart disease database collected in the regular medical practice. Medical expert evaluation of some preliminary inductive learning results have demonstrated that explicit detection of outliers can be useful for maintaining the data quality of medical records and that it might be a key for the improvement of medical decisions and their reliability in the regular medical practice. With the intention of on-line detection of possible data inconsistences, sets of confirmation rules have been developed for the database and their test results are reported in this work.