Assertion modeling and its role in clinical phenotype identification

This paper describes an approach to assertion classification and an empirical study on the impact this task has on phenotype identification, a real world application in the clinical domain. The task of assertion classification is to assign to each medical concept mentioned in a clinical report (e.g., pneumonia, chest pain) a specific assertion category (e.g., present, absent, and possible). To improve the classification of medical assertions, we propose several new features that capture the semantic properties of special cue words highly indicative of a specific assertion category. The results obtained outperform the current state-of-the-art results for this task. Furthermore, we confirm the intuition that assertion classification contributes in significantly improving the results of phenotype identification from free-text clinical records.

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► We built a state-of-the-art NLP system for the classification of medical assertions.
► Assertion focus features have a positive impact in modeling assertion classification.
► Assertion classification has a significant role in improving pneumonia identification.
► Statistical feature selection is suitable for phenotype identification applications.
► Better results are obtained when mapping the hedge classes to negative pneumonia.