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Updated: Jan 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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在ST升高心肌梗塞后的一年生存预测模型:对Cox脆弱模型和机器学习进行比较分析

Mansour Rezaei1, Maryam Montaseri2, Shayan Mostafaei3

  • 1Social Development and Health Promotion Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Caspian journal of internal medicine
|December 12, 2025
PubMed
概括

有脆弱性的考克斯模型最好预测ST升高心肌梗塞 (STEMI) 患者的1年死亡率. 该模型解释了未观察到的患者差异,与随机生存森林和生存支持向量回归等其他方法相比,改善了生存预测.

关键词:
脆弱模型的脆弱性模型.机器学习就是机器学习.肌肉心脏病发作 肌肉心脏病发作对生存分析的分析.

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