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, cilt.16, sa.1, ss.109-115, 2009 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 1
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1197/jamia.m2950
  • Dergi Adı: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.109-115
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.