Application of Bayesian networks to generate synthetic health data | Zendy

Dhamanpreet Kaur | Zendy; Matthew Sobiesk | Zendy; Shubham Patil | Zendy; Jin Liu | Zendy; Puran Bhagat | Zendy; Amar Gupta | Zendy; Natasha Markuzon | Zendy

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Application of Bayesian networks to generate synthetic health data

Author(s) -

Dhamanpreet Kaur,

Matthew Sobiesk,

Shubham Patil,

Jin Liu,

Puran Bhagat,

Amar Gupta,

Natasha Markuzon

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa303

Subject(s) - computer science , bayesian probability , bayesian network , artificial intelligence , synthetic data , machine learning , data mining , data science

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.

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