C4.5: programs for machine learning
C4.5: programs for machine learning
Computers and Biomedical Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Ontology-based information extraction: An introduction and a survey of current approaches
Journal of Information Science
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Substantial medical data such as discharge summaries and operative reports are stored in textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. Terms that appear in these documents tend to appear in different contexts. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are those denied by the patient or subsequently “ruled out.” Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. In this paper we examine the applicability of a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task of similar medical narratives and show its advantages. The new algorithm can be applied also to further context identification and information extraction tasks.