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Negation Scope Detection in Clinical Notes and Scientific Abstracts: A Feature-enriched LSTM-based Approach.

Elena Sergeeva1, Henghui Zhu2, Peter Prinsen3

  • 1Massachusetts Institute of Technology, Cambridge, MA, USA.

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Summary
This summary is machine-generated.

This study introduces a new neural network model to accurately detect negation in clinical texts, distinguishing between present and absent medical conditions. This advancement improves the analysis of Electronic Health Records for better clinical insights.

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Area of Science:

  • Natural Language Processing
  • Biomedical Informatics
  • Machine Learning

Background:

  • Electronic Health Records (EHRs) contain valuable clinical data.
  • Clinical narratives within EHRs detail medical conditions and findings.
  • Distinguishing negated from non-negated events is crucial for accurate prognosis.

Purpose of the Study:

  • To develop a feature-enriched neural network model for negation scope detection in biomedical texts.
  • To enhance the analysis of clinical narratives for improved clinical decision-making.

Main Methods:

  • A novel neural network architecture was employed.
  • The model was enriched with specific features to improve negation detection.
  • The system was evaluated on scientific abstracts and radiology reports.

Main Results:

  • The model achieved state-of-the-art performance on scientific abstracts from the BioScope1 corpus without needing gold cue information.
  • A competitive performance was achieved on radiology reports.
  • Robust high performance was demonstrated across different text types.

Conclusions:

  • The proposed model effectively performs negation scope detection in biomedical texts.
  • This technology has significant potential for improving the interpretation of clinical narratives in EHRs.
  • The model's ability to perform without gold cue information enhances its practical applicability.