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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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NLPReViz: an interactive tool for natural language processing on clinical text.

Gaurav Trivedi1, Phuong Pham2, Wendy W Chapman3

  • 1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.

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|October 11, 2017
PubMed
Summary

Domain experts can improve clinical text analysis using a new tool for natural language processing (NLP) model revision. Physician feedback significantly enhanced model performance on colonoscopy quality variables.

Keywords:
electronic health recordsmachine learningmedical informaticsnatural language processing (NLP)user-computer interface

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing
  • Health Informatics

Background:

  • Extracting insights from clinical text is challenging due to the expertise gap between domain specialists and NLP professionals.
  • Natural language processing (NLP) models require refinement to accurately capture information from unstructured clinical notes.

Purpose of the Study:

  • To introduce a prototype tool enabling interactive review and revision of NLP models for binary concepts in clinical text.
  • To assess the impact of domain expert feedback on the performance of NLP models for clinical data.

Main Methods:

  • A prototype tool was developed for interactive NLP model building and revision by domain experts.
  • A user study involved 9 physicians who utilized the tool to develop and refine models for two colonoscopy quality variables.
  • Performance metrics, including F1-scores, were tracked in relation to the amount of expert feedback provided.

Main Results:

  • Expert review significantly improved NLP model performance, achieving F1-scores between 0.78-0.91 for "appendiceal-orifice" and 0.88-0.94 for "biopsy".
  • Performance gains ranged from 13.26% to 29.90% for "appendiceal-orifice" and -1.52% to 11.74% for "biopsy" with small initial training sets.
  • The tool achieved an average System Usability Scale score of 70.56, indicating good usability.

Conclusions:

  • Interactive revision by domain experts is effective in enhancing NLP model accuracy for clinical text analysis.
  • The developed tool facilitates collaboration between clinical experts and NLP, bridging the expertise gap.
  • Further design improvements are suggested based on user feedback to optimize the tool for clinical applications.