| Prototypical cases and adaptation rules for diagnosis of dysmorphic syndromes. | |
| | |
MedLine Citation:
|
PMID: 16160252 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
|
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 most 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. |
| | |
Authors:
|
Rainer Schmidt; Tina Waligora |
Publication Detail:
|
Type: Journal Article |
Journal Detail:
|
Title: Studies in health technology and informatics Volume: 116 ISSN: 0926-9630 ISO Abbreviation: Stud Health Technol Inform Publication Date: 2005 |
Date Detail:
|
Created Date: 2005-09-14 Completed Date: - Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 9214582 Medline TA: Stud Health Technol Inform Country: Netherlands |
Other Details:
|
Languages: eng Pagination: 157-62 Citation Subset: T |
Affiliation:
|
Institut für Medizinische Informatik und Biometrie, University of Rostock Rostock, Germany. |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
|
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Structuring Clinical Guidelines through the Recognition of Deontic Operators.
Next Document: Knowledge Discovery on Functional Disabilities: Clustering Based on Rules versus other Approaches.