Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Subword variation in text message classification
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automated syndrome classification using early phase emergency department data
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Journal of Biomedical Informatics
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Objective:: To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance. Methods:: We preprocessed chief complaints using two preprocessors (CCP and EMT-P) and evaluated whether classification performance increased for a probabilistic classifier (CoCo) or for a keyword-based classifier (modification of the NYC Department of Health and Mental Hygiene chief complaint coder (KC)). Results:: CCP exhibited high accuracy (85%) in preprocessing chief complaints but only slightly improved CoCo's classification performance for a few syndromes. EMT-P, which splits chief complaints into multiple problems, substantially increased CoCo's sensitivity for all syndromes. Preprocessing with CCP or EMT-P only improved KC's sensitivity for the Constitutional syndrome. Conclusion:: Evaluation of preprocessing systems should not be limited to accuracy of the preprocessor but should include the effect of preprocessing on syndromic classification. Splitting chief complaints into multiple problems before classification is important for CoCo, but other preprocessing steps only slightly improved classification performance for CoCo and a keyword-based classifier.