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Representing information in patient reports using natural language processing and the extensible markup language

C Friedman1, G Hripcsak, L Shagina

  • 1Department of Medical Informatics, Columbia University, New York, New York 10032, USA. friedma@flux.cpmc.columbia.edu

Journal of the American Medical Informatics Association : JAMIA
|January 30, 1999
PubMed
Summary

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This study developed an XML-based document model and natural language processing (NLP) method to efficiently structure clinical information from patient reports. The system successfully processed 199 out of 200 reports, enabling better data retrieval and analysis.

Area of Science:

  • Medical Informatics
  • Computational Linguistics

Background:

  • Clinical information is often unstructured in patient reports, hindering efficient data retrieval and application.
  • Existing methods for structuring clinical data can be time-consuming and lack integration with original report content.

Purpose of the Study:

  • To design a reliable and efficient document model for accessing clinical information in patient reports.
  • To implement an automated natural language processing (NLP) method for mapping textual reports to a structured model.

Main Methods:

  • Designed an Extensible Markup Language (XML) document model with a Document Type Definition (DTD).
  • Modified an existing NLP system to generate XML output consistent with the model.
  • Processed 200 patient reports using the modified NLP system and validated the XML output.

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Main Results:

  • The NLP system successfully processed all 200 reports.
  • 199 out of 200 reports generated valid XML output consistent with the DTD.

Conclusions:

  • NLP can automate the creation of enriched documents with structured components linked to original text.
  • This integrated model enhances accurate and efficient retrieval of specific information from clinical reports.
  • The XML format facilitates the use of readily available software tools for document manipulation.