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Related Experiment Video

Updated: Jul 5, 2026

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Optimizing annotation resources for natural language de-identification via a game theoretic framework.

Muqun Li1, David Carrell2, John Aberdeen3

  • 1Dept. of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.

Journal of Biomedical Informatics
|March 30, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a game theory framework to optimize healthcare organizations' investments in de-identifying electronic medical records (EMRs). The model helps balance privacy risks and data utility, ensuring better decision-making for data sharing.

Keywords:
Electronic medical recordsGame theoryNatural language processingPrivacy

Related Experiment Videos

Last Updated: Jul 5, 2026

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Area of Science:

  • Health Informatics
  • Computational Linguistics
  • Game Theory

Background:

  • Electronic medical records (EMRs) are increasingly used for research and policy analysis.
  • De-identifying EMRs is crucial for privacy but automated tools have limitations.
  • Over-investment in de-identification technology may occur due to assumptions about adversaries' capabilities.

Purpose of the Study:

  • To design a natural language de-identification framework using game theory.
  • To enable healthcare organizations (HCOs) to optimize de-identification investments.
  • To balance privacy risks with data utility for EMR data sharing.

Main Methods:

  • Introduced a Stackelberg game as a cost-benefit model for de-identification.
  • Evaluated the model using 2100 clinical notes from Vanderbilt University Medical Center.
  • Simulated policy alternatives, including de-identification model training costs and data utility loss.

Main Results:

  • With a limited budget ($2000), precision and recall were 0.86 and 0.8, respectively.
  • A game-based approach improved privacy and utility, achieving 0.77 precision and 0.61 recall while deterring attackers.
  • The model identified scenarios where data should not be released due to high risk.

Conclusions:

  • A game theoretic framework optimizes decision-making for EMR de-identification investments.
  • This approach allows HCOs to effectively manage privacy risks and data utility before sharing EMR data.