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DIAGAID: a connectionists approach to determine the information value of clinical data
University of Turku, Department of Clinical Chemistry, Central Laboratory, Turku University Central Hospital, Turku, Finland.
Department of Computer Science, Åbo Akademi, Åbo, Finland.
Department of Computer Science, Åbo Akademi, Åbo, Finland.
Department of Computer Science, Åbo Akademi, Åbo, Finland.
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1991 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 3, no 4, 193-201 p.Article in journal (Refereed) Published
Abstract [en]

In clinical medicine the diagnosis is usually based on several signs and symptoms and some laboratory test results. It would be of great benefit if we could learn the diagnostic criteria from example cases and represent systematically the most important findings supporting or rejecting the diagnosis. In rare diseases where the physician may not be familiar with the ailment the scoring of symptoms would be very useful. In this paper we describe a connectionist approach based on Minsky-Papert's perceptrons for evaluation of the information value of clinical data in diagnosing the Nephropathia epidemica. The scores are compared with those from the Bayesian approach and from the evaluation by an experienced clinician.

Place, publisher, year, edition, pages
Elsevier, 1991. Vol. 3, no 4, 193-201 p.
Keyword [en]
Neural networks, perceptron, expert systems, diagnosis, Nephropathia epidemica
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-80549DOI: 10.1016/0933-3657(91)90011-YOAI: oai:DiVA.org:umu-80549DiVA: diva2:650369
Available from: 2013-09-20 Created: 2013-09-20 Last updated: 2017-12-06Bibliographically approved

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Eklund, Patrik

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