CLAMP

CLAMP

CLAMP is an advanced clinical NLP toolkit designed for the recognition and automatic encoding of clinical information from narrative patient reports. It offers a suite of customizable components, including named entity recognition and assertion detection, enabling users to annotate documents and generate tailored models. Built on award-winning methodologies, CLAMP integrates seamlessly with the UIMA framework, leveraging resources like medical dictionaries and abbreviation lists to enhance clinical text processing.

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Top CLAMP Features

  • Clinical information encoding
  • Proven clinical NLP methods
  • Custom model training
  • Annotation tool integration
  • Import clinical text corpora
  • NLP pipeline construction
  • Named entity recognition
  • Assertion detection capabilities
  • UMLS encoding support
  • Built-in knowledge resources
  • cTAKES compatibility
  • Lower linguistic level annotations
  • User-friendly workspace
  • Comprehensive documentation available
  • Evaluation of custom models
  • Flexible component customization
  • Multi-task NLP capabilities
  • Award-winning methodologies
  • Support for various clinical challenges
  • Research-backed development.