BibTeX

@inProceedings{kokkinakis-2011-health-141311,
	title        = {Health Portals and Clinical Phenotypes - Recognition using SNOMED CT},
	abstract     = {The medical domain is particularly well endowed with various sources of terminology. Usually, such sources vary with respect to size, structure, depth and breadth of descriptive power, granularity and applicability. This paper investigates the extent by which the largest available medical nomenclature for Swedish can cope with a particularly challenging and difficult to automatically acquire type of terminology, namely (clinical) phenotypes. We evaluated the content of the resource on extracted reference symptom lists from several popular health portals. The results indicate that a large number of such phenotypes are expressed using figurative language, or contextualized using a number of variant expressions. SNOMED CT cannot easily accommodate for such variation and vagueness expressed in real text data, unless we devise means to handle such variation, e.g. by the use of near synonym dictionaries, development and linking of consumer health vocabularies. The presented research has several implications since accurate identification of phenotypes can for instance increase the value of available data in decision making and thus allow automatic systems to dynamically correct inappropriate domain decisions.},
	booktitle    = {9th Scandinavian Conference on Health Informatics},
	author       = {Kokkinakis, Dimitrios},
	year         = {2011},
}