dc.contributor.author | Bourlas, P | en |
dc.contributor.author | Giakoumakis, E | en |
dc.contributor.author | Papakonstantinou, G | en |
dc.date.accessioned | 2014-03-01T01:47:46Z | |
dc.date.available | 2014-03-01T01:47:46Z | |
dc.date.issued | 1999 | en |
dc.identifier.uri | https://dspace.lib.ntua.gr/xmlui/handle/123456789/25317 | |
dc.subject | Continuous Improvement | en |
dc.subject | Electrocardiogram | en |
dc.subject | Knowledge Acquisition | en |
dc.subject | Knowledge Base | en |
dc.subject | Machine Learning | en |
dc.subject | Management System | en |
dc.subject | Rule Induction | en |
dc.subject | Decision Tree | en |
dc.title | A knowledge acquisition and management system for ECG diagnosis | en |
heal.type | journalArticle | en |
heal.publicationDate | 1999 | en |
heal.abstract | . We present a knowledge acquisition and management system for 12-lead electrocardiogram (ECG)diagnosis, which, using rule induction techniques, continuously improves its initial knowledge base(developed with the cooperation of several ECG experts), interacting with the user; advising him about theECG diagnosis and taking advantage of his response. To achieve all these, special machine learning and othertechniques are used: Learning of new | en |
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