Machine Learning and Rule-based Approaches to Assertion Classification
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Uzuner-2009-Machine Learning and.pdf
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Author(s)
Uzuner, Ozlem
Zhang, Xiaoran
Sibanda, Tawanda
Date Issued
2009
Journal
Journal of the American Medical Informatics Association
Publisher
BMJ Publishing Group
Citation
Uzuner, Özlem, Xiaoran Zhang, and Tawanda Sibanda. “Machine Learning and Rule-based Approaches to Assertion Classification.” Journal of the American Medical Informatics Association 16.1 (2009): 109-115. © 2009, British Medical Journal Publishing Group
Version
Final published version
Abstract
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 the patient, or associated with someone other than the patient. The authors use these two systems to (1) extend negation and uncertainty extraction to recognition of alter-association assertions, (2) determine the contribution of lexical and syntactic context to assertion classification, and (3) test if a machine learning approach to assertion classification can be as generally applicable and useful as its rule-based counterparts.
Measurements The authors evaluated assertion classification approaches with precision, recall, and F-measure.
Results The ENegEx algorithm is a general algorithm that can be directly applied to new corpora. Despite being based on machine learning, StAC can also be applied out-of-the-box to new corpora and achieve similar generality.
Conclusion The StAC models that are developed on discharge summaries can be successfully applied to radiology reports. These models benefit the most from words found in the ± 4 word window of the target and can outperform ENegEx.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
http://dx.doi.org/10.1197/jamia.M2950
