Application of Bayesian networks to generate synthetic health data.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Mar 18, 2021

Abstract

OBJECTIVE: This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data.

Authors

  • Dhamanpreet Kaur
    Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  • Matthew Sobiesk
    Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  • Shubham Patil
    Rochester Institute of Technology, Rochester, New York, USA.
  • Jin Liu
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Puran Bhagat
    Philips Research North America, Briarcliff Manor, NY.
  • Amar Gupta
    Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  • Natasha Markuzon
    Clinical Informatics, Philips Research North America, Cambridge, Massachusetts, USA.

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