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Towards a comprehensive medical language processing system: methods and issues

C Friedman1

  • 1Department of Computer Science, Queens College CUNY, USA.

Proceedings : a Conference of the American Medical Informatics Association. AMIA Fall Symposium
|January 1, 1997
PubMed
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Natural language processing (NLP) systems offer coded clinical data but lack broad application. This study extends the MedLEE NLP system to encompass comprehensive clinical information for wider real-world use.

Area of Science:

  • Clinical informatics
  • Medical data processing
  • Natural Language Processing

Background:

  • Natural language processing (NLP) systems can automate data entry for clinical applications by extracting coded data from text.
  • Current NLP systems show promise but lack widespread adoption due to limited domain coverage and performance in real-world settings.

Purpose of the Study:

  • To describe methods and challenges in extending an operational NLP system (MedLEE) to a comprehensive clinical information domain.
  • To enhance NLP system applicability for practical, broad clinical use.

Main Methods:

  • Ongoing extension of the MedLEE NLP system.
  • Focus on achieving broad domain coverage for comprehensive clinical information.
  • Addressing issues related to real-world application performance.

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Main Results:

  • Methods and issues associated with extending MedLEE are described.
  • The extension aims to encompass comprehensive clinical information.
  • The goal is to enable wider practical application of NLP in healthcare.

Conclusions:

  • Extending NLP systems like MedLEE to comprehensive clinical domains is crucial for practical, widespread adoption.
  • Addressing domain breadth and real-world performance is key to overcoming current limitations.
  • Successful extension can significantly improve clinical data management and application.