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 (SCI-Expanded)

• Publication Type: Article / Article
• Volume: 16 Issue: 1
• Publication Date: 2009
• Doi Number: 10.1197/jamia.m2950
• Journal Name: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
• Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
• Page Numbers: pp.109-115
• Middle East Technical University Affiliated: No

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.