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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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Cite This
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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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.