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Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical

Leibo Liu1, Oscar Perez-Concha1, Anthony Nguyen2

  • 1Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.

Interactive Journal of Medical Research
|August 25, 2023
PubMed
Summary

This study introduces DEFT, a web-based system for deidentifying electronic medical record text. DEFT enhances privacy by accurately removing personally identifiable information (PII) and improving research data usability.

Keywords:
deep learningdeidentificationelectronic health recordselectronic medical recordsfree texthuman in the loopmachine learningnarrative free textunstructured dataweb-based system

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Area of Science:

  • Health Informatics
  • Medical Data Privacy
  • Natural Language Processing

Background:

  • Electronic medical records (EMRs) contain valuable clinical data for research.
  • Protecting patient privacy necessitates deidentifying personally identifiable information (PII) in EMR free text.
  • Manual deidentification is inefficient, driving the need for automated solutions.

Purpose of the Study:

  • Develop an accurate, user-friendly, web-based system for deidentifying EMR free text, named DEFT.
  • Enhance adoption in real-world settings through features like interactive learning and collaboration.
  • Facilitate secondary use of clinical data while maintaining patient privacy.

Main Methods:

  • DEFT utilizes a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) deep learning model with RoBERTa embeddings.
  • An interactive learning loop with preannotation accelerates manual deidentification.
  • The system supports project management, customizable PII types, and user access control.

Main Results:

  • DEFT achieved superior performance on the 2014 i2b2 dataset, with microaverage F1-scores of 0.9627.
  • Real-world application on clinical notes yielded a microaverage F1-score of 0.9507.
  • Preannotation increased manual annotation efficiency by 43%.

Conclusions:

  • DEFT provides an accessible solution for deidentifying EMR free text for researchers and data custodians.
  • The system's interactive learning loop and user-friendly interface lower the technical barrier for deidentification tasks.
  • DEFT facilitates secure and efficient utilization of clinical narrative data.