Synthesize Extremely High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model

Synthetic electronic health records (EHRs) that are both realistic and preserve privacy can serve as an alternative to real EHRs for machine learning (ML) modeling and statistical analysis. However, generating high-fidelity and granular electronic health record (EHR) data in its original, highly-dim...

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Detalles Bibliográficos
Autores principales: Theodorou, Brandon, Xiao, Cao, Sun, Jimeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029081/
https://www.ncbi.nlm.nih.gov/pubmed/36945542
http://dx.doi.org/10.21203/rs.3.rs-2644725/v1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029081/
https://www.ncbi.nlm.nih.gov/pubmed/36945542
http://dx.doi.org/10.21203/rs.3.rs-2644725/v1

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