BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework

Clinical texts, such as discharge summaries or test reports, contain a valuable amount of information that, if efficiently and effectively mined, could be used to infer new knowledge, possibly leading to better diagnosis and therapeutics. With this in mind, the SemEval-2014 Analysis of Clinical Text task aimed at assessing and improving current methods for identification and normalization of concepts occurring in clinical narrative. This paper describes our approach in this task, which was based on a fully modular architecture for text mining. We followed a pure dictionary-based approach, after performing error analysis to refine our dictionaries. We obtained an F-measure of 69.4% in the entity recognition task, achieving the second best precision over all submitted runs (81.3%), with above average recall (60.5%). In the normalization task, we achieved a strict accuracy of 53.1% and a relaxed accuracy of 87.0%.