Generating synthetic mixed discrete-continuous health records with mixed sum-product networks.

ABSTRACT:

Objective

Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate synthetic data that can be shared for subsequent statistical analysis. This anonymization method was evaluated with respect to privacy and information loss.

Materials and methods

Using a simulation study, information loss was quantified by assessing whether synthetic data could reproduce regression parameters obtained from the original data. Predictors variable types were varied between continuous, count, categorical, and mixed discrete-continuous. Additionally, we measured whether the MSPN approach successfully anonymizes the data by re

SUBMITTER: Kroes SKS 

PROVIDER: S-EPMC9748584 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature