{"ID":2886908,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02771","arxiv_id":"2508.02771","title":"Synthetic medical data generation: state of the art and application to trauma mechanism classification","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.02771","url_pdf":"https://arxiv.org/pdf/2508.02771v1","authors":"[\"Océane Doremus\",\"Ariel Guerra-Adames\",\"Marta Avalos-Fernandez\",\"Vianney Jouhet\",\"Cédric Gil-Jardiné\",\"Emmanuel Lagarde\"]","published":"2025-08-04T12:59:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}