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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.

Associated Institutions

Text Machine Lab for NLP, Dept of Computer Science, UMass Lowell

Application Domains
  • Clinical records
Other Resource Type
Software Subtype
  • Data mining / Machine learning
  • Named entity recognition
  • NLP / information extraction
Programming Languages
  • Python
Operating Systems
Included Components
  • Application Programming Interface
  • Pipeline workflow
Dataset Subtype
Data Model Subtype
Online Resource Subtype
Knowledge Base Subtype
Intended User Types
  • Informatics researcher
  • NLP researcher or developer

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,

Available Documentation
  • Included demo/tutorial data
  • Web page/HTML documentation
Licensing Type
Free software: Apache v2.0 license