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Natural language processing and text analysis methods offer the potential of uncovering hidden associations from large amounts of unprocessed texts. The SemEval-2015 Analysis of Clinical Text task aimed at fostering research on the application of these methods in the clinical domain. The proposed task consisted of disorder identification with normalization to SNOMED-CT concepts, and disorder attribute identification, or template filling. We participated in both sub-tasks, using a combination of machine-learning and rules for recognizing and normalizing disease mentions, and rule-based methods for template filling. We achieved an F-score of 71.2% in the entity recognition and normalization task, and a slot weighted accuracy of 69.5% in the template filling task.

BioinformaticsUA: Machine Learning and Rule-Based Recognition of Disorders and Clinical Attributes from Patient Notes