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Related Experiment Videos

Natural language processing and its future in medicine.

C Friedman1, G Hripcsak

  • 1Department of Computer Science, Queens College, City University of New York, USA. friedman.carol@columbia.edu

Academic Medicine : Journal of the Association of American Medical Colleges
|September 25, 1999
PubMed
Summary

Natural language processing (NLP) structures text-based clinical information for improved patient care and lower costs. NLP systems are advancing, with some already deployed in practical medical applications.

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Area of Science:

  • Medical Informatics
  • Computational Linguistics

Background:

  • Accurate electronic clinical information can enhance patient care and reduce costs.
  • Current clinical data is often unstructured natural language text, limiting electronic retrieval.
  • Structuring clinical information is essential for automated applications.

Purpose of the Study:

  • To explore the role of Natural Language Processing (NLP) in structuring clinical text.
  • To review the development and application of NLP systems in medicine.
  • To discuss the future potential of NLP in healthcare.

Main Methods:

  • Review of Natural Language Processing (NLP) principles and applications in medicine.
  • Description of recently developed NLP systems for clinical text analysis.

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  • Discussion of challenges and advancements in NLP development.

    • Main Results:

    • NLP can extract and structure text-based clinical information, making it electronically accessible.
    • Developed NLP systems show promising performance in clinical settings.
    • Some NLP systems have been successfully deployed for practical use.

    Conclusions:

    • NLP is a key technology for unlocking the potential of unstructured clinical data.
    • Continued development of NLP systems is crucial for advancing automated healthcare applications.
    • The future of NLP in medicine holds significant promise for improving patient outcomes and efficiency.