通过对听觉资料的联合合并整合听觉数据集
Samira Saak1,2, Dirk Oetting2,3, Birger Kollmeier1,2,3
1Medizinische Physik, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Trends in hearing
|June 30, 2025
概括
这项研究将来自多个听觉数据集的听觉概况 (AP) 合并,以创建全面的患者概况. 结合数据可以增强对听力损失模式的理解,并提高诊断准确度.
科学领域:
- 听力学 听力学是指听力学.
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 听力学数据集提供了对听力损失的见解.
- 之前的工作是从单个数据集创建审计资料 (AP).
- 分析多个数据集对于全面的患者人口估计至关重要.
研究的目的:
- 通过合并步骤扩展AP生成管道.
- 基于听力学测量相似性的不同数据集的AP结合起来.
- 为改进听力损失分析创建一个统一的AP集.
主要方法:
- 使用相似度评分将13个AP (N=595) 与31个AP (N=1,272) 合并.
- 使用共同特征的重叠密度用于合并.
- 开发了随机森林模型,使用各种听觉测量组合.
主要成果:
- 提出了一套新的13个AP组合.
- 在组合的配置文件中实现了令人满意的分类性能.
- 发现结合声量测量,听力图和语音测试产生了最好的结果.
结论:
- 增强的管道成功地将跨数据集的AP合并在一起.
- 这种方法可以将其推广到其他数据集,以形成一个全局的个人资料集.
- 分类模型保持临床适用性并改善诊断洞察力.
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