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

2025/08/04 by Doremus, Océane, Guerra-Adames, Ariel, Avalos-Fernandez, Marta +3
#FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2508.02771

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.

Citations

• Evaluation of synthetic electronic health records: A systematic review and experimental assessment
• Generative Adversarial Networks
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

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