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Updated: Jul 5, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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可解释的机器学习用于预测慢性病进展风险和预测慢性病进展风险

Jin-Xin Zheng1, Xin Li1, Jiang Zhu2

  • 1Department of Nephrology, Ruijin Hospital, Institute of Nephrology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Digital health
|January 18, 2024
PubMed
概括

可解释的机器学习模型准确地预测慢性病 (CKD) 的进展. 极端梯度提升和随机生存森林提供了卓越的性能和对年龄和肌素等风险因素的洞察力.

关键词:
慢性脏疾病 慢性脏疾病可解释的模型可以解释模型.机器学习是机器学习.随机生存森林的森林.生存分析的分析.

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