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We present an NLP system that classifies the assertion type of medical problems in clinical notes used for the Fourth i2b2/VA Challenge. Our classifier uses a variety of linguistic features, including lexical, syntactic, lexicosyntactic, and contextual features. To overcome an extremely unbalanced distribution of assertion types in the data set, we focused our efforts on adding features specifically to improve the performance of minority classes. | Improving Classification of Medical Assertions in Clinical Notes Youngjun Kim School of Computing University of Utah Salt Lake City UT youngjun@cs.utah.edu Ellen Riloff School of Computing University of Utah Salt Lake City UT riloff@cs.utah.edu Stéphane M. Meystre Department of Biomedical Informatics University of Utah Salt Lake City UT stephane.meystre@hsc.utah.edu Abstract We present an NLP system that classifies the assertion type of medical problems in clinical notes used for the Fourth i2b2 VA Challenge. Our classifier uses a variety of linguistic features including lexical syntactic lexico-syntactic and contextual features. To overcome an extremely unbalanced distribution of assertion types in the data set we focused our efforts on adding features specifically to improve the performance of minority classes. As a result our system reached 94.17 micro-averaged and 79.76 macro-averaged F1-measures and showed substantial recall gains on the minority classes. 1 Introduction Since the beginning of the new millennium there has been a growing need in the medical community for Natural Language Processing NLP technology to provide computable information from narrative text and enable improved data quality and decision-making. Many NLP researchers working with clinical text i.e. documents in the electronic health record are also realizing that the transition to machine learning techniques from traditional rule-based methods can lead to more efficient ways to process increasingly large collections of clinical narratives. As evidence of this transition nearly all of the best-performing systems in the Fourth i2b2 VA Challenge Uzuner and DuVall 2010 used machine learning methods. 311 In this paper we focus on the medical assertions classification task. Given a medical problem mentioned in a clinical text an assertion classifier must look at the context and choose the status of how the medical problem pertains to the patient by assigning one of six labels present absent .
