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Related Experiment Video

Updated: Oct 16, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

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A Context-Enhanced De-identification System.

Kahyun Lee1, Mehmet Kayaalp2, Sam Henry1

  • 1George Mason University, Fairfax, VA, USA.

ACM Transactions on Computing for Healthcare
|October 22, 2021
PubMed
Summary
This summary is machine-generated.

A new context-enhanced de-identification (CEDI) system overcomes limitations of sentence-by-sentence processing by incorporating context embeddings. CEDI improves accuracy in de-identifying clinical reports by capturing cross-sentence dependencies.

Keywords:
HIPAAde-identificationentity recognitioninformation extractionnatural language processing

Related Experiment Videos

Last Updated: Oct 16, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.6K

Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Machine Learning

Background:

  • Modern de-identification systems often use bidirectional long short-term memory (biLSTM) and conditional random field (CRF), processing text sentence-by-sentence.
  • This sentence-based approach limits capturing dependencies across sentence boundaries, a common issue in clinical reports.
  • Accurate sentence boundary detection is crucial but challenging in clinical text due to abundant cross-sentence co-references and dependencies.

Purpose of the Study:

  • To develop a novel de-identification system that overcomes the limitations of sentence boundary detection.
  • To enhance existing state-of-the-art systems by incorporating context beyond individual sentences.
  • To improve the accuracy and robustness of de-identification in clinical narrative reports.

Main Methods:

  • Developed a new system, Context-Enhanced De-Identification (CEDI), based on the NeuroNER framework.
  • Incorporated context embeddings using forward and backward n-grams, eliminating the need for sentence boundary detection.
  • Enhanced the CEDI system with deep affix features and an attention mechanism to capture relevant input features.

Main Results:

  • The CEDI system demonstrated superior performance compared to NeuroNER on three de-identification datasets: 2006 i2b2, 2014 i2b2, and 2016 CEGS N-GRID.
  • Statistical significance (p < 0.01) was observed across all tested datasets, indicating a reliable improvement.
  • Further enhancements to CEDI using deep affix features and an attention mechanism led to additional performance gains.

Conclusions:

  • The CEDI system effectively captures dependencies over sentence boundaries, addressing a key limitation of previous methods.
  • By bypassing sentence boundary detection, CEDI offers a more robust approach for de-identifying complex clinical reports.
  • The enhanced CEDI system represents a significant advancement in automated clinical data de-identification.