An interpretable machine learning model for predicting myocardial injury in patients with high cervical spinal cord injury

An interpretable machine learning model for predicting myocardial injury in patients with high cervical spinal cord injury

BackgroundHigh cervical spinal cord injury (HCSCI) is associated with severe autonomic dysfunction and an increased risk of cardiovascular complications, including myocardial injury. However, early identification of myocardial injury remains challenging because of the lack of predictive tools.Method...

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Bibliographic Details
Main Authors: Jiaqi Li, Bingyu Zhang, Ye Liao, Liqin Wei, Qinfeng Huang, Lijun Lin, Jiaxin Chen, Hui Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Genetics
Subjects:
high cervical spinal cord injury
myocardial injury
machine learning
neural network
SHAP
risk prediction
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2025.1636065/full
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https://www.frontiersin.org/articles/10.3389/fgene.2025.1636065/full

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