Machine Learning and Rule-based Approaches to Assertion Classification

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Date

2009-01-01

Author

Uzuner, Oezlem
Zhang, Xiaoran
Sibanda, Tawanda

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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.

Subject Keywords

Health Informatics

URI

https://hdl.handle.net/11511/66514

Journal

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

DOI

https://doi.org/10.1197/jamia.m2950

Collections

Engineering, Article

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Citation Formats

O. Uzuner, X. Zhang, and T. Sibanda, “Machine Learning and Rule-based Approaches to Assertion Classification,” JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, pp. 109–115, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66514.