FASEB journal : official publication of the Federation of American Societies for Experimental BiologyAgedFemaleHumansMaleMiddle Aged
An Interpretable Machine Learning Model Based on Metabolomics for Predicting Plaque Burden in Cryptogenic Stroke.
Zi-Miao Liu, Yin-Yu Zi, Xiao-Yu Cheng, Ning-Yuan Liu, Zhong Ji, Yi-Hua He, Ling Li, Meng-Jia Yang, Chao Dang, Ming Yi, Ying-Xin He, Xin-Guang Yang, Yong-Teng Xu, Zhen-Zhou Lin, Jia-Jia Zhu
Published: 202510.1096/fj.202503352R
Abstract
Cryptogenic stroke represents 25%-40% of ischemic strokes, with many cases harboring unrecognized large artery atherosclerosis (LAA) requiring specific secondary prevention. In this multicenter pilot study, we developed a metabolomics-based machine l…
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