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GHOSTS: Validated generation of synthetic hospital time series
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DOI：10.1016/j.artmed.2026.103443.png)
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Abstract
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• This study presents novel generative adversarial network with structured sparsity constrains. • It is used for generation of uneven and heterogeneous time series and static attributes. • Synthetic ICU data were used to train classifier to distinguish between low and high SOFA scores. • Experiments on MIMIC-IV and eICU datasets validated our method’s results.
Keywords:
Generative models
Generative adversarial model
Electronic health records
Synthetic data
Time series
Privacy
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