International journal of surgery (London, England)
Machine-learning approach uncovers hemodynamic-driven phenotypes in cardiac surgery by clustering multimodal, high-dimensional perioperative data.
Siyu Kong, Xiao Ning, Jie Sun, Ke Ding, Jing Hu, Xiao Zhou, Yali Ge, Xuesheng Liu, Fan Yang, Zhimin Zhang, Lihai Chen, Hongwei Shi, Jifang Zhou
Published: 202510.1097/JS9.0000000000004536
Abstract
BACKGROUND: A critical limitation of current risk assessment in cardiac surgery is its reliance on static preoperative models, which fail to account for dynamic intraoperative physiological changes. This limitation may lead to the oversight of critic…
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