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Comparing natural language processing tools to extract medical problems from narrative text.

Stéphane M Meystre1, Peter J Haug

  • 1Department of Medical Informatics, University of Utah, Salt Lake City, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
PubMed
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This study developed a Natural Language Processing (NLP) system to automatically extract medical problems from patient records. An improved MetaMap Transfer (MMTx) with negation detection achieved the best performance, enhancing problem list accuracy.

Area of Science:

  • Medical Informatics
  • Natural Language Processing

Background:

  • Maintaining accurate patient problem lists is crucial for effective healthcare.
  • Manual problem list updates are time-consuming and prone to errors.

Purpose of the Study:

  • To develop and compare Natural Language Processing (NLP) applications for automatically extracting medical problems from electronic health records.
  • To evaluate the effectiveness of different NLP approaches in improving problem list completeness and accuracy.

Main Methods:

  • Three NLP applications were evaluated: MetaMap Transfer (MMTx) with negation detection (NegEx), a local application (MPLUS2), and keyword searching.
  • These systems were trained to identify 80 specific diagnosis-type medical problems from free-text clinical notes.
  • The MMTx with NegEx system underwent improvements, including disambiguation and enhanced negation detection.

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Main Results:

  • The improved MMTx with NegEx achieved the highest recall (0.892) and precision (0.753).
  • Keyword searching showed high precision (0.807) but lower recall (0.575).
  • MPLUS2 demonstrated lower performance compared to the improved MMTx system.

Conclusions:

  • Automated NLP systems can significantly improve the accuracy and timeliness of patient problem lists.
  • The enhanced MMTx with NegEx approach shows strong potential for clinical application in problem list management.