Cargando…
The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures
BACKGROUND: Clinical data synthesis aims at generating realistic data for healthcare research, system implementation and training. It protects patient confidentiality, deepens our understanding of the complexity in healthcare, and is a promising tool for situations where real world data is difficult...
Autores principales: | Chen, Junqiao, Chun, David, Patel, Milesh, Chiang, Epson, James, Jesse |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416981/ https://www.ncbi.nlm.nih.gov/pubmed/30871520 http://dx.doi.org/10.1186/s12911-019-0793-0 |
Ejemplares similares
-
Synthea™ Novel coronavirus (COVID-19) model and synthetic data set
por: Walonoski, Jason, et al.
Publicado: (2020) -
Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
por: Walonoski, Jason, et al.
Publicado: (2018) -
Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data
por: Suresh, Krish, et al.
Publicado: (2023) -
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study
por: El Emam, Khaled, et al.
Publicado: (2022) -
Can synthetic data be a proxy for real clinical trial data? A validation study
por: Azizi, Zahra, et al.
Publicado: (2021)