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Automated revision of expert rules for treating acute abdominal pain in children

Moustakis Vasilis, Sašo Džeroski, Giorgos Potamias, Giorgos Charissis

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URIhttp://purl.tuc.gr/dl/dias/4821839F-B2B0-4FE4-B401-2E08C48933B4-
Identifierhttps://doi.org/10.1007/BFb0029440-
Languageen-
TitleAutomated revision of expert rules for treating acute abdominal pain in childrenen
CreatorMoustakis Vasilisen
CreatorΜουστακης Βασιληςel
CreatorSašo Džeroskien
CreatorGiorgos Potamiasen
CreatorGiorgos Charissisen
PublisherSpringer Verlagen
Content SummaryDecision making knowledge acquired directly from a medical expert is often incorrect and incomplete. Another source of knowledge about a decision making problem are examples of expert decisions in situations that have occurred in practice, stored in patient records of clinical information systems. Such examples can be used to revise the expert-provided knowledge, i.e., to discover and repair its deficiences. The revised knowledge performs better than the original one and often better than rules learned from examples alone. In addition, it inherits parts of the original expert knowledge and is thus easier to understand and accept for the expert. We present an application of the machine learning approach of theory revision to the problem of revising an expert-provided theory for treating children with acute abdominal pain.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-04-
Date of Publication1997-
Bibliographic CitationS. Dzeroski, G. Potamias, V. Moustakis, G. Charissis, "Automated Revision of Expert Rules for Treating Acute Abdominal Pain in Children," in 6th Conference on Artificial Intelligence in Medicine Europe, 1997, pp. 98-109. doi: 10.1007/BFb0029440en

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