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Updated: Jan 22, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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通过可解释的机器学习模型探索全血细胞计数衍生的炎症生物标志物和癌症发病率之间的关联:基于NHANES 1999至2016年的研究

Sijun Zhao1, Ping Fu1, Liangqing Lin1

  • 1Department of Traumatology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Province, China.

Medicine
|January 21, 2026
PubMed
概括

全血细胞计数 (CBC) 衍生的炎症标志物如SIRI,NLR,MLR和NMLR与癌症风险增加有关. 可解释的机器学习揭示了复杂的非线性关系,表明了改进癌症检测的潜力.

关键词:
由CBC衍生的炎症生物标志物尼汉斯 (NHANES) 是一个名人.这就是 SHAP SHAP 的意思.癌症 癌症 癌症 癌症 癌症机器学习是机器学习.

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