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Two Approaches to Assertion Classification

By Özlem Uzuner, Xiaoran Zhang and Tawanda Sibanda

Abstract

We present a study of two approaches to assertion classification: one of these approaches, Extended NegEx, extends the rule-based NegEx algorithm to cover alter-association assertions; the other, SNegEx, is a machine learning approach and explores the contribution of lexical and syntactic context to assertion classification. Both approaches determine whether a problem, as asserted in a patient record, is present, absent, or uncertain in the patient, or associated with someone other than the patient

Topics: Articles
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:2656003
Provided by: PubMed Central
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