Machine Learning and Rule-based Approaches to Assertion Classification

Uzuner, Oezlem; Zhang, Xiaoran; Sibanda, Tawanda

doi:10.1197/jamia.m2950

Machine Learning and Rule-based Approaches to Assertion Classification

Uzuner O., Zhang X., Sibanda T.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, cilt.16, sa.1, ss.109-115, 2009 (SCI-Expanded, SSCI, Scopus)

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
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
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.