Machine Learning and Rule-based Approaches to Assertion Classification | Zendy

Özlem Uzuner | Zendy; Xiang Zhang | Zendy; Thulani Sibanda | Zendy

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Machine Learning and Rule-based Approaches to Assertion Classification

Author(s) -

Özlem Uzuner,

Xiang Zhang,

Thulani Sibanda

Publication year - 2008

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1197/jamia.m2950

Subject(s) - assertion , computer science , artificial intelligence , machine learning , classifier (uml) , generality , natural language processing , context (archaeology) , negation , programming language , psychology , paleontology , psychotherapist , biology

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.

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