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

Natural language processing and text analy-sis methods offer the potential of uncovering hidden associations from large amounts of un-processed texts. The SemEval-2015 Analy-sis of Clinical Text task aimed at fostering re-search on the application of these methods in the clinical domain. The proposed task con-sisted of disorder identification with normal-ization to SNOMED-CT concepts, and disor-der attribute identification, or template filling. We participated in both sub-tasks, using a combination of machine-learning and rules for recognizing and normalizing disease men-tions, 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.