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arXiv · 2508.02771

Synthetic medical data generation: state of the art and application to trauma mechanism classification

Océane Doremus·Ariel Guerra-Adames·Marta Avalos-Fernandez·Vianney Jouhet·Cédric Gil-Jardiné·Emmanuel Lagarde
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Abstract

Faced with the challenges of patient confidentiality and scientific reproducibility, research on machine learning for health is turning towards the conception of synthetic medical databases. This article presents a brief overview of state-of-the-art machine learning methods for generating synthetic tabular and textual data, focusing their application to the automatic classification of trauma mechanisms, followed by our proposed methodology for generating high-quality, synthetic medical records combining tabular and unstructured text data.

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Océane Doremus, Ariel Guerra-Adames, Marta Avalos-Fernandez, Vianney Jouhet, Cédric Gil-Jardiné, Emmanuel Lagarde. 2025-08-04. Synthetic medical data generation: state of the art and application to trauma mechanism classification. https://arxiv.org/abs/2508.02771

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