Disorder Normalization in Clinical Notes with DNorm

Abstract. We describe an application of DNorm – a mathematically principled and high performing methodology for disease recognition and normalization, even in the presence of term variation – to clinical notes. DNorm consists of a text processing pipeline, including the BANNER named entity recognizer to lo-cate diseases in the text, and a novel machine learning approach based on pair-wise learning to rank to normalize the recognized mentions to concepts within a controlled lexicon. DNorm achieved the second highest performance in Task 1a (named entity recognition) and the highest performance (strict accuracy) in Task 1b (normalization). A web-based demonstration of DNorm is available a