title: Automatic SNOMED classification--a corpus-based method.
abstract: This paper presents a method of automatic classification of clinical narrative through text comparison. A diagnosis report can be classified by searching archive texts that show a high textual similarity, and the 'nearest neighbor classifies the case. This paper describes the method's theoretical background and gives implementation details. Large scale simulation experiments were run with a wide range of histology reports. Results showed that for 80-84% of the trials, relevant classification lines were included among the first five alternatives. In 5% of the cases, retrieval was unsuccessful due to the absence of relevant archive reports. From the results it is concluded that the method is a versatile approach for finding potentially good classifications.
authors.affiliation: Department of Medical Informatics, Maastricht University, The Netherlands. berry@mi.unimaas.nl
authors.forename: L M
authors.initials: LM
authors.lastname: de Bruijn
authors.forename: A
authors.initials: A
authors.lastname: Hasman
authors.forename: J W
authors.initials: JW
authors.lastname: Arends
mesh_terms: D000465:Algorithms; D002965:Classification; D003198:Computer Simulation; D003628:Database Management Systems; D003933:Diagnosis; D001330:Electronic Data Processing; D005568:Forms and Records Control; D006653:Histology; D006801:Humans; D016247:Information Storage and Retrieval; D008499:Medical Records; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated