Abstract
Since diagnosis of dysmorphic syndromes is a domain with incomplete knowledge and where even experts have seen only few syndromes themselves during their lifetime, documentation of cases and the use of case-oriented techniques are popular. In dysmorphic systems, diagnosis usually is performed as a classification task, where a prototypicality measure is applied to determine the most probable syndrome. These measures differ from the usual Case-Based Reasoning similarity measures, because here cases and syndromes are not represented as attribute value pairs but as long lists of symptoms, and because query cases are not compared with cases but with prototypes. In contrast to these dysmorphic systems our approach additionally applies adaptation rules. These rules do not only consider single symptoms but combinations of them, which indicate high or low probabilities of specific syndromes.
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Schmidt, R., Waligora, T. (2006). Using Prototypes and Adaptation Rules for Diagnosis of Dysmorphic Syndromes. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_1
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DOI: https://doi.org/10.1007/11790853_1
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