Umesh Chaithra, Mahendra Manjunath, Bej Saptarshi, Wolkenhauer Olaf, Wolfien Markus
Institute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
School of Data Science, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, India.
Pflugers Arch. 2025 Apr;477(4):531-542. doi: 10.1007/s00424-024-03024-w. Epub 2024 Oct 17.
Recent advancements in generative approaches in AI have opened up the prospect of synthetic tabular clinical data generation. From filling in missing values in real-world data, these approaches have now advanced to creating complex multi-tables. This review explores the development of techniques capable of synthesizing patient data and modeling multiple tables. We highlight the challenges and opportunities of these methods for analyzing patient data in physiology. Additionally, it discusses the challenges and potential of these approaches in improving clinical research, personalized medicine, and healthcare policy. The integration of these generative models into physiological settings may represent both a theoretical advancement and a practical tool that has the potential to improve mechanistic understanding and patient care. By providing a reliable source of synthetic data, these models can also help mitigate privacy concerns and facilitate large-scale data sharing.
人工智能生成方法的最新进展为合成表格临床数据的生成带来了前景。从填补现实世界数据中的缺失值开始，这些方法现已发展到创建复杂的多表。本综述探讨了能够合成患者数据并对多个表格进行建模的技术的发展。我们强调了这些方法在生理分析患者数据方面的挑战和机遇。此外，还讨论了这些方法在改善临床研究、个性化医疗和医疗政策方面的挑战和潜力。将这些生成模型整合到生理环境中可能代表着一种理论进步和一种有可能改善机理理解和患者护理的实用工具。通过提供可靠的合成数据来源，这些模型还可以帮助减轻隐私担忧并促进大规模数据共享。
