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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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Published on: September 20, 2018

A common type system for clinical natural language processing.

Stephen T Wu1, Vinod C Kaggal, Dmitriy Dligach

  • 1Mayo Clinic, Rochester, Rochester, MN, USA. wu.stephen@mayo.edu.

Journal of Biomedical Semantics
|January 5, 2013
PubMed
Summary
This summary is machine-generated.

A new type system for clinical Natural Language Processing (NLP) enables deep semantics, improving interoperability between structured and unstructured electronic medical record data.

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing

Background:

  • Electronic medical records contain heterogeneous clinical data.
  • Clinical Natural Language Processing (NLP) is crucial for standardizing clinical text.
  • A common type system is needed for data interoperability.

Purpose of the Study:

  • Define a common type system for clinical NLP.
  • Enable interoperability between structured and unstructured clinical data.
  • Facilitate data reuse across different clinical settings.

Main Methods:

  • Developed a common type system for clinical NLP.
  • Targeted deep semantics using Clinical Element Models (CEMs).
  • Implemented the type system in UIMA and cTAKES (versions 2.0+).

Main Results:

  • A functional type system for clinical NLP has been created.
  • The system supports deep semantics, enabling knowledge encapsulation from text.
  • Achieved interoperability with structured data types through CEM-based semantics.

Conclusions:

  • The developed type system facilitates sharing of NLP-derived knowledge with heterogeneous clinical data.
  • CEM-based semantics provide a point of interoperability for structured and unstructured data.
  • This approach moves beyond surface semantics to build in deep clinical meaning.