机器学习模型的开发和比较,用于预测有听力损失的患者的中度至重度耳
Chenguang Zhang1,2, Tao Ran1,2, Yicong Wang3
1Qinghai University, Xining, China.
Frontiers in neurology
|January 28, 2026
概括
机器学习模型可以预测听力损失患者的临床显著的耳声. 随机森林模型,考虑到听力损失严重程度和年龄等因素,证明了最高的准确性.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 医疗信息学 医疗信息学
- 心理学 心理学 心理学
背景情况:
- 临床显著的耳 (THI评分≥38) 是患有听力损失的患者常见的问题.
- 识别 tinnitus 的预测因素对于早期干预和管理至关重要.
研究的目的:
- 分析与听力障碍患者临床显著耳相关的心理和临床因素.
- 构建和比较四个机器学习 (ML) 算法对耳风险的预测性能.
主要方法:
- 利用了听力损失患者的电子医疗记录.
- 使用单变量分析,LASSO回归和Boruta算法选预测变量.
- 开发并验证了使用后勤回归,随机森林,XGBoost和SVM的预测模型,通过AUC和DeLong测试比较性能.
主要成果:
- 确定了9个关键变量,包括年龄,高血压,睡眠障碍,焦虑,听力损失严重程度,抑郁,噪音暴露,听力侧面和耳毒药物使用.
- 随机森林 (RF) 模型实现了最高的预测性能 (验证集中的AUC=0.977),超过了后勤回归和SVM.
- SHAP分析显示,听力损失的严重程度,年龄和睡眠障碍是最有影响力的预测因素.
结论:
- 随机森林模型在预测临床意义上的耳声方面表现出卓越的准确性.
- 整合听力和心理因素,如听力损失严重程度,年龄和睡眠障碍,对于早期风险识别至关重要.
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