Machine Learning and Rule-based Approaches to Assertion Classification


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Uzuner O., Zhang X., Sibanda T.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, vol.16, no.1, pp.109-115, 2009 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2009
  • Doi Number: 10.1197/jamia.m2950
  • Title of Journal : JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
  • Page Numbers: pp.109-115

Abstract

Objectives: The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical Assertion Classifier (StAC), presents a machine learning solution to assertion classification.