Work

An Interpretable Machine Learning Model Based on Metabolomics for Predicting Plaque Burden in Cryptogenic Stroke

Year: 2025

Type: article

Study design: Observational Study

Source: The FASEB Journal

DOI: 10.1096/fj.202503352r

Authors: 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, Jia Yin, Yong‐Ming Wu, Liang Zhou, Jin‐Sheng Zeng, Su‐Yue Pan, Zhen Deng

Institutions: Nanfang Hospital, Southern Medical University, Sun Yat-sen University, Sun Yat-sen Memorial Hospital, The First Affiliated Hospital, Sun Yat-sen University

Language: English

Topic: Acute Ischemic Stroke Management

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China, Basic and Applied Basic Research Foundation of Guangdong Province

Awards: G4730134698, G7778498642, G8492743862, G8731001855, G962786081

OpenAlex ID: W4416795066

FWCI: 0.603

Cites: 26

Cited by: 1

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