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Testing tactics to localize de-identification.

Cyril Grouin1, Arnaud Rosier, Olivier Dameron

  • 1Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur, Centre National de la Recherche Scientifique (LIMSI-CNRS), Orsay, France.

Studies in Health Technology and Informatics
|September 12, 2009
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Summary
This summary is machine-generated.

De-identification (anonymisation) systems require local tuning. Developing a new French system outperformed adapting an English one, achieving 83% recall and 92% precision for patient reports.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Data Management

Background:

  • Renewed interest in de-identification (anonymisation) has spurred US system development.
  • De-identification systems need local tuning for specific document sets and languages.
  • Existing English-based de-identification tools may not directly translate to other languages like French.

Purpose of the Study:

  • To address challenges in de-identifying French patient reports.
  • To evaluate two distinct methods for adapting de-identification systems to a new language.
  • To minimize the need for language engineers to work with sensitive identified patient data.

Main Methods:

  • A two-step approach was implemented: initial gross de-identification within the hospital, followed by system development.
  • Method 1: Adapting an existing US English de-identifier by localizing patterns.
  • Method 2: Developing a new de-identification system using the same underlying methods but tailored for French.

Main Results:

  • Adapting the English de-identifier proved cumbersome and yielded suboptimal performance.
  • Developing a new French-specific system, with similar effort, achieved significantly better results.
  • The new French system demonstrated 83% recall and 92% precision on a test set of 23 clinical texts.

Conclusions:

  • Developing a new de-identification system tailored to the target language and document type is more effective than adapting existing English systems.
  • A gross de-identification step at the source (hospital) can facilitate tuning by language engineers.
  • The developed French de-identification system shows promising performance for anonymizing clinical texts.