Research Paper: Machine Learning and Rule-based Approaches to Assertion Classification

@article{Uzuner2009ResearchPM,
  title={Research Paper: Machine Learning and Rule-based Approaches to Assertion Classification},
  author={{\"O}zlem Uzuner and Xiaoran Zhang and Tawanda C. Sibanda},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  year={2009},
  volume={16 1},
  pages={
          109-15
        }
}
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. DESIGN For each mention of each medical problem, both approaches determine whether the problem, as asserted by the context of that mention, is present, absent, or uncertain in… 

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