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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.02771 |
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| _version_ | 1866912519580286976 |
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| author | Doremus, Océane Guerra-Adames, Ariel Avalos-Fernandez, Marta Jouhet, Vianney Gil-Jardiné, Cédric Lagarde, Emmanuel |
| author_facet | Doremus, Océane Guerra-Adames, Ariel Avalos-Fernandez, Marta Jouhet, Vianney Gil-Jardiné, Cédric Lagarde, Emmanuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthetic medical data generation: state of the art and application to trauma mechanism classification Doremus, Océane Guerra-Adames, Ariel Avalos-Fernandez, Marta Jouhet, Vianney Gil-Jardiné, Cédric Lagarde, Emmanuel Machine Learning 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. |
| title | Synthetic medical data generation: state of the art and application to trauma mechanism classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.02771 |