Application of Bayesian networks to generate synthetic health data.

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.

Materials and methods

We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning

SUBMITTER: Kaur D 

PROVIDER: S-EPMC7973486 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature