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Learning debiased graph representations from the OMOP common data model for synthetic data generation.
- Source :
- BMC Medical Research Methodology; 6/22/2024, Vol. 24 Issue 1, p1-13, 13p
- Publication Year :
- 2024
-
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 discovery (DYNOTEARS, J-PCMCI+, LiNGAM) and map the outputs into Synthea graphs. The graphs are evaluated quantitatively by their individual and relative complexity and qualitatively by medical experts. Results: The algorithms were found to learn qualitatively and quantitatively different graph representations. Whereas the Markov chain results in extremely large graphs, TARM, DYNOTEARS, and J-PCMCI+ were found to reduce the data dimension during learning. The MultiGroupDirect LiNGAM algorithm was found to not be applicable to the problem statement at hand. Conclusion: Only TARM and DYNOTEARS are practical algorithms for real-world data in this use case. As causal discovery is a method to debias purely statistical relationships, the gradient-based causal discovery algorithm DYNOTEARS was found to be most suitable. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712288
- Volume :
- 24
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Medical Research Methodology
- Publication Type :
- Academic Journal
- Accession number :
- 178026817
- Full Text :
- https://doi.org/10.1186/s12874-024-02257-8