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A Five-Step Workflow to Manually Annotate Unstructured Data into Training Dataset for Natural Language Processing.

Yunshu Zhu1, Ting Song1, Zhenyu Zhang1

  • 1Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, New South Wales, Australia.

Studies in Health Technology and Informatics
|January 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a five-step workflow to improve manual annotation of electronic health records (EHRs) for Natural Language Processing (NLP). The developed method achieved 96% accuracy, enhancing NLP model training.

Keywords:
Electronic health recordsannotationannotation workflowmachine learningnatural language processingtraining data development

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Data Annotation

Background:

  • High-quality annotated datasets are crucial for Natural Language Processing (NLP) performance in analyzing electronic health records (EHRs).
  • Current methods for guiding manual annotation of unstructured EHR data are insufficient, potentially limiting NLP advancements.

Purpose of the Study:

  • To develop and evaluate a structured five-step workflow for the manual annotation of unstructured EHR datasets.
  • To address the need for effective methods in creating annotated corpora for NLP applications in healthcare.

Main Methods:

  • A five-step annotation workflow was developed: (1) annotator training, (2) vocabulary identification, (3) schema development, (4) annotation execution, and (5) result validation.
  • The workflow was applied to annotate agitation symptoms within EHRs from 40 Australian residential aged care facilities.

Main Results:

  • The application of the proposed workflow resulted in a highly accurate annotated corpus, achieving a 96% accuracy rate.
  • Demonstrated the effectiveness of the systematic approach in manual data processing for creating training data.

Conclusions:

  • The proposed five-step annotation workflow provides an effective framework for manual data processing in creating annotated training corpora.
  • This methodology can significantly improve the development of Natural Language Processing algorithms for extracting information from electronic health records.