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"更好的耳朵"的自我感知与听力测量相匹配:来自机器学习建模的见解
1Department of Languages and Literature, University of Nicosia, Nicosia, Cyprus.
Noise & health
|March 9, 2026
概括
当耳朵之间的听力差异很大时,人们会准确地识别出他们更好的听觉耳朵. 听力不对称的大小,而不是它的方向,是自我意识的关键,有助于临床决策.
科学领域:
- 听力学 听力学是指听力学.
- 医疗保健中的机器学习
- 公共卫生监督 公共卫生监督
背景情况:
- 对听力不对称的准确自我认知对于临床决策和沟通至关重要.
- 客观听力测量和主观意识之间的联系尚未得到充分理解.
研究的目的:
- 使用机器学习,模拟正确识别听力更好的耳朵的概率.
- 分析客观听力测量模式与主观意识的听力不对称之间的关系.
主要方法:
- 使用了美国国家健康和营养检查调查数据.
- 训练了一台光梯度增强机器分类器,使用纯色调平均值 (PTA) 和声间不对称度量.
- 预测正确的主观识别更好听觉的耳朵.
主要成果:
- 该模型实现了高性能 (精度为0.85,F1得分为0.91).
- 耳间PTA不对称的绝对大小是准确的自我报告的主要预测指标.
- 不对称的方向对正确的自我认同的影响最小.
结论:
- 听力不对称性的主观意识与差异的大小相关,而不是方向.
- 自我报告的"更好的耳朵"捕获了重要的听力测量信息,这对分拣和监视有用.
- 对于轻度不对称的病例,建议谨慎,因为错误分类的可能性更高.
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