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渐变增强决策树的预测价值,用于肺损伤患者手术后的骨质疏松症并发症

Zhongming Tang1, Jifu Tang1, Wei Liu1

  • 1Department of Thoracic Surgery, Chest Hospital of Guangxi Zhuang Autonomous Region Liuzhou, Guangxi Zhuang Autonomous Region, China.

American journal of translational research
|August 8, 2024
PubMed
概括

渐变增强决策树 (GBDT) 模型有效地预测了肺部损坏的患者的手术后骨质疏松症. 这种机器学习方法在较小的患者队伍中显示出对并发症预测的前景.

关键词:
被摧毁的肺部被摧毁了.这种病叫做 atelectasis.梯度增强决策树的决定树.机器学习是机器学习.预测 预测 预测 预测

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