Open-source natural language processing system for named entity recognition in clinical text of electronic health records. CliNER will identify clinically-relevant entities mentioned in a clinical narrative (such as diseases/disorders, signs/symptoms, medications, procedures, etc). Requires annotated data such as the i2b2 2010 NLP data set.
Anna Rumshisky, Tristan Neumann, William Boag, Kevin Wacome.
Text Machine Lab for NLP, Dept of Computer Science, UMass Lowell
W. Boag, K. Wacome, T. Naumann, A. Rumshisky. CliNER: A Lightweight Tool for Clinical
Concept Extraction. (poster) AMIA Joint Summits on Clinical Research Informatics 2015. San Francisco,