Machine Learning and Rule-based Approaches to Assertion Classification

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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.