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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 458-462
Roni Romano , Tel-Aviv University, Tel-Aviv, Israel
Lior Rokach , Ben Gurion University, Beer Sheva 84105, Israel
Oded Maimon , Tel-Aviv University, Tel-Aviv, Israel
ABSTRACT
Substantial electronically stored textual data such as clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. 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. We present a new cascaded pattern learning method for automatic identification of negative context in clinical narratives re-ports. Studying the training corpuses, the classification errors and patterns selected by the classifier, we noticed that it is possible to create a more powerful ensemble structure than the structure obtained from general-purpose ensemble method (such as Adaboost). We compare the new algorithm to previous methods proposed for the same task of similar medical narratives, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy.
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CITATION

O. Maimon, R. Romano and L. Rokach, "Cascaded Data Mining Methods for Text Understanding, with medical case study," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 458-462.
doi:10.1109/ICDMW.2006.38
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