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Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic

Kory Kreimeyer1, Matthew Foster1, Abhishek Pandey1

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This review identified 71 clinical natural language processing (NLP) systems for extracting structured data from clinical text. While many tasks are addressed, challenges remain in temporal information extraction and concept normalization.

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Area of Science:

  • Informatics
  • Biomedical Informatics
  • Computational Linguistics

Background:

  • Unstructured clinical free text poses challenges for data analysis and research.
  • Clinical Natural Language Processing (NLP) systems aim to convert this text into structured information.
  • A comprehensive overview of existing clinical NLP systems is needed to guide future development.

Purpose of the Study:

  • To systematically identify and analyze existing clinical NLP systems.
  • To evaluate the capabilities of these systems in generating structured information from unstructured clinical text.
  • To identify areas of success and challenges in current clinical NLP applications.

Main Methods:

  • Systematic literature search using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Searched seven literature databases with keywords related to NLP and structured data capture.
  • Two reviewers screened 7149 records, analyzing 86 papers detailing 71 distinct clinical NLP systems.

Main Results:

  • Identified 71 clinical NLP systems addressing diverse clinical and research tasks.
  • Found that existing systems adequately address certain tasks.
  • Highlighted challenges in areas like temporal information extraction and concept normalization to standard terminologies.

Conclusions:

  • Numerous clinical NLP systems exist for processing free text into structured data.
  • The review provides valuable insights for prioritizing future development in clinical NLP.
  • Further research is needed to address identified challenges in specific NLP tasks.