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A large dataset of annotated incident reports on medication errors.

Zoie S Y Wong1,2, Neil Waters3, Jiaxing Liu4

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A new dataset of 58,658 medication error reports, machine-annotated for named entity recognition and intention analysis, aids automated learning from patient safety incidents.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Patient Safety

Background:

  • Medication errors are critical for patient safety, but unstructured text in incident reports hinders analysis.
  • Automated analysis requires large, annotated datasets for natural language processing (NLP) model development.

Purpose of the Study:

  • To present a large, machine-annotated corpus of medication error incident reports.
  • To facilitate the development of NLP models for automated information extraction and incident learning.

Main Methods:

  • Developed a machine annotation process for 58,658 medication error reports.
  • Performed cross-validation for named entity recognition (NER) and intention/factuality analysis.
  • Detailed annotation workflow and technical validation procedures.

Main Results:

  • Achieved F1-scores of 0.97 for NER and 0.76 for intention/factuality analysis.
  • Annotated dataset includes 478,175 named entities.
  • Successfully differentiated incident types by recognizing intended vs. actual occurrences.

Conclusions:

  • The presented corpus enables advanced information extraction for medication error analysis.
  • The dataset and machine annotator support future development in patient safety learning.
  • Automated analysis of medication error reports can be significantly improved with this resource.