将聚类噪声数据与医院患者满意度相关联
Kenton Hummel1, Erica Ryherd1, Xiaoyue Cheng2
1Durham School of Architectural Engineering & Construction, University of Nebraska-Lincoln, Omaha, Nebraska 68182-0681, USA.
The Journal of the Acoustical Society of America
|August 24, 2023
概括
医院噪音会影响患者的满意度. 机器学习识别了噪音模式,揭示了较高的活跃声音水平和较不安静的夜间时间与较低的患者满意度相关,与传统的噪音指标不同.
科学领域:
- 医疗保健中的声学
- 机器学习在医疗保健中的应用
背景情况:
- 医院噪音是一个持续的问题,影响患者的体验和满意度.
- 传统的噪声指标可能无法完全捕捉医院声学环境的复杂性.
- 了解噪音模式对于改善患者福祉至关重要.
研究的目的:
- 应用无监督机器学习集群来分析医院的声学环境.
- 研究噪音模式与患者感知之间的关系.
- 确定与患者满意度相关的新型声学指标.
主要方法:
- 在三个医院单位的9个病房进行了24小时的声学测量.
- 无监督机器学习 (k-means集群) 用于将噪音分类为"活跃"和"非活跃"状态.
- 患者满意度数据与来自集群的新型声学指标相关联.
主要成果:
- 使用k-means集群识别了两个不同的噪音集群",活跃"和"非活跃".
- 像等效声压水平 (LAeq) 这样的传统指标与患者的感知没有显著的相关性.
- 较低的患者满意度与更高的"活跃声音水平",更高的"总活跃百分比"和更低的"夜间安静百分比"显著相关.
结论:
- 统计集群提供了比传统指标更深入地了解医院声学环境.
- 特定的噪音模式,如活动噪声增加和夜间安静减少,会对患者的满意度产生负面影响.
- 从聚类噪声数据中获得的新型指标可以更准确地评估对患者体验的声学影响.
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