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    科学领域:

    • 耳鼻喉科 耳鼻喉科 耳鼻喉科
    • 生物医学工程 生物医学工程
    • 数据科学数据科学数据科学

    背景情况:

    • 耳植入物结果显示出广泛的变异性,传统方法解释了不到20%的变异.
    • 预测个体患者的耳植入物成功仍然是一个重大的临床挑战.

    研究的目的:

    • 与传统线性方法相比,评估机器学习 (ML) 模型在预测耳植入物结果方面的有效性.
    • 确定影响耳植入物性能变化的关键因素.

    主要方法:

    • 一项回顾性研究分析了15个中心2251名成年耳植入体接受者的数据.
    • 七个ML模型,包括极端梯度提升 (XGBoost),与线性回归进行了比较.
    • 模型被优化并使用网格搜索,交叉验证和SHapley添加式扩展 (SHAP) 来确定特征的重要性进行验证.

    主要成果:

    • 与线性回归 (p=0.003) 相比,XGBoost表现最好,预测误差减少了4.11%.
    • 所有集体ML方法在预测语音识别分数方面显著优于线性回归.
    • 确定了重要的预测因素是耳植入物使用的持续时间,植入时的年龄,听力损失的持续时间和手术前的分数.

    结论:

    • 机器学习模型在预测耳植入物结果方面提供了适度的改进.
    • 超过80%的结果差异仍然无法解释,这表明需要超越常规临床数据的新型预测因素.