耳风险因素及其随时间的演变
Lise Hobeika1,2,3,4, Matt Fillingim5,6, Christophe Tanguay-Sabourin5,7,8
1Université Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, Paris, France. lise.hobeika@gmail.com.
Nature communications
|May 7, 2025
概括
机器学习模型将听力健康确定为 tinnitus 存在和严重性的关键因素. 简化问卷现在可以帮助检测有严重耳风险的个体,以便进行早期干预.
科学领域:
- 听力学 听力学是指听力学.
- 计算健康 计算健康
- 心理学 心理学 心理学
背景情况:
- 主观耳,一种没有外部声音的听觉感知,缺乏明确的风险因素,阻碍了预防和管理.
- 了解耳预测因素对于开发有效的干预措施和改善患者治疗结果至关重要.
研究的目的:
- 开发和验证用于预测耳存在和严重性的机器学习模型.
- 确定声的关键社会人口统计学,心理和健康相关预测因素.
- 创建一个临床工具,用于早期识别有严重耳风险的个体.
主要方法:
- 利用英国生物库数据集 (192,993名参与者) 训练两种机器学习模型,预测耳的存在和严重程度.
- 纳入社会人口统计,心理和健康相关数据作为预测因素.
- 在Tinnitus Research Initiative的独立数据集上验证了耳严重程度模型.
主要成果:
- 听力健康成为 tinnitus 存在和严重程度的主要风险因素.
- 心理因素,包括情绪,神经症和睡眠,显著预测了耳的严重程度.
- 严重程度模型在9年内预测耳进展的准确性很高 (ROC=0.78).
结论:
- 机器学习有效地预测着耳的存在和严重程度,强调了听力健康和心理因素的重要性.
- 一个经过验证的六项调查问卷可以识别患有严重耳风险的个人.
- 早期检测和支持性护理对于那些有发展严重耳风险的人来说至关重要.
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