Learning debiased graph representations from the OMOP common data model for synthetic data generation.

ABSTRACT:

Background

Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention.

Methods

Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method. For this study, data pipelines were built which extract data from OMOP, convert them into time series format, learn temporal rules by 2 statistical algorithms (Markov chain, TARM) and 3 algorithms of causal

SUBMITTER: Schulz NA 

PROVIDER: S-EPMC11193245 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature