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A novel pipeline for realistic synthetic longitudinal EHR data generation | Synapse
March 3, 2026
Open Access
A novel pipeline for realistic synthetic longitudinal EHR data generation
GJ
Gabrielle A. Josling
Commonwealth Scientific and Industrial Research Organisation
ID
Ibrahima Diouf
Commonwealth Scientific and Industrial Research Organisation
SK
S. Khanna
Commonwealth Scientific and Industrial Research Organisation
Puntos clave
Synthetic data generation enhances the realism of longitudinal data models, benefiting research validity.
The generated electronic health records mimic real patient data, allowing for effective simulations and analyses.
This assessment employs a novel pipeline that streamlines data generation processes in a systematic manner.
Overall, this innovative technique may facilitate improved healthcare data analysis, warranting external validation.
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Josling et al. (Thu,) studied this question.
synapsesocial.com/papers/69a75cb9c6e9836116a25d86
https://doi.org/https://doi.org/10.21203/rs.3.rs-8497559/v1
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