CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines

Ergin Soysal, Jingqi Wang, Min Jiang, Yonghui Wu, Serguei Pakhomov, Hongfang Liu, Hua Xu

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding. We also demonstrate the efficiency of the CLAMP graphic user interface in building customized, high-performance NLP pipelines with 2 use cases, extracting smoking status and lab test values. CLAMP is publicly available for research use, and we believe it is a unique asset for the clinical NLP community.

Original languageEnglish (US)
Pages (from-to)331-336
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume25
Issue number3
DOIs
StatePublished - Mar 1 2018

Keywords

  • Clinical text processing
  • Machine learning
  • Natural language processing

ASJC Scopus subject areas

  • Health Informatics

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