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Related Experiment Videos

Extending a medical language processing system to the functional status domain.

Michael Bales1, Rita Kukafka, Ann Burkhardt

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
PubMed
Summary

Automated coding of functional status information (FSI) using natural language processing (NLP) can reduce costs. This study adapted an NLP system to assign International Classification of Functioning, Disability, and Health (ICF) codes from health records.

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Medical Classification Systems

Background:

  • The International Classification of Functioning, Disability, and Health (ICF) is a WHO standard for health records.
  • Manual coding of ICF is time-consuming and expensive.
  • Automated coding methods are needed to improve efficiency.

Purpose of the Study:

  • To investigate the use of Natural Language Processing (NLP) for automated ICF coding.
  • To adapt an existing NLP system for coding Functional Status Information (FSI).

Main Methods:

  • An existing NLP system for clinical information encoding was utilized.
  • The system's lexicon and coding table were modified.
  • Preprocessing and postprocessing programs were developed for automated ICF code assignment.

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Main Results:

  • The adapted NLP system successfully assigned selected ICF codes.
  • The automated approach offers a potential solution to the cost of manual ICF coding.

Conclusions:

  • NLP can be effectively adapted for automated ICF coding of FSI.
  • This method has the potential to reduce the burden and cost associated with manual ICF coding in health records.