利用患者及其代孕护理人员与临床医生的沟通来预测息治疗决策:一个语音处理研究
Jiyoun Song1, Homayoon Beigi2, Anahita Davoudi3
1University of Pennsylvania School of Nursing, Department of Biobehavioral Health Sciences, Philadelphia, PA, USA.
概括
语音分析可以预测管理长期护理 (MLTC) 设置中的息治疗 (PC) 偏好. 患者与临床医生的电话中能量和音调较高表明PC接受,有助于护理规划.
科学领域:
- 老年学是一门学科.
- 语音语言病理学 语言病理学
- 医疗信息学 医疗信息学
背景情况:
- 管理长期护理 (MLTC) 为复杂患者提供综合服务.
- 在MLTC内整合息护理 (PC) 是至关重要的,但初始沟通是研究不足的.
- 了解患者对PC的偏好对于有效的护理提供至关重要.
研究的目的:
- 开发一种计算管道,用于分析患者与临床医生的交互中的语音特征.
- 根据语音模式预测患者对息护理 (PC) 的偏好.
- 识别与PC接受或拒绝相关的声学特征.
主要方法:
- 分析了79个记录的患者-临床医生电话 (15.4小时).
- 从通话中提取声学特征和语言内容.
- 语音特征与人口统计和临床数据的整合用于预测建模.
主要成果:
- 53%的参与者接受了PC,而47%的人拒绝了.
- 更高的语音能量和音调水平与PC接受度相关 (0.065对0.59和289对260Hz).
- 梯度提升实现了最高的预测性能,F-score为65.5%.
结论:
- 语音处理算法可以有效地分析患者与临床医生的对话.
- 特定的语音特征与息治疗 (PC) 偏好有关.
- 这种方法为改善MLTC的沟通和护理计划提供了潜力.
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