Chaithra Umesh, Manjunath Mahendra, Saptarshi Bej, Olaf Wolkenhauer, Markus Wolfien
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引用次数: 0
Abstract
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.
期刊介绍：
Pflügers Archiv European Journal of Physiology publishes those results of original research that are seen as advancing the physiological sciences, especially those providing mechanistic insights into physiological functions at the molecular and cellular level, and clearly conveying a physiological message. Submissions are encouraged that deal with the evaluation of molecular and cellular mechanisms of disease, ideally resulting in translational research. Purely descriptive papers covering applied physiology or clinical papers will be excluded. Papers on methodological topics will be considered if they contribute to the development of novel tools for further investigation of (patho)physiological mechanisms.
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