使用语音识别和机器学习技术来检测患者报告的息护理患者的对话语音结果
Lei Dong1, Hideyuki Hirayama1, XueJiao Zheng1
1Department of Palliative Nursing, Health Sciences, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
Japan journal of nursing science : JJNS
|January 8, 2025
概括
语音识别和机器学习显示了在息治疗中自动评估患者报告的结果的潜力. 虽然患者语音识别率较低,但这项技术可以减少医疗保健提供者的数据输入负担.
科学领域:
- 抚慰性护理是一种缓解性护理.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 患者报告的结果测量 (PROM) 在息治疗中对症状评估至关重要.
- 手动输入PROM数据到电子医疗记录对医疗保健提供者来说是负担.
- 语音识别技术为简化PROM数据收集提供了一个潜在的解决方案.
研究的目的:
- 评估使用语音识别和机器学习用于自动化PROM评估的可行性.
- 分析来自息护理患者采访的语音数据,以检测症状.
主要方法:
- 采访了100名家庭息护理患者,使用综合息护理结果表 (IPOS).
- 语音数据被转录,并计算患者的语音识别率.
- 开发了一个机器学习模型来检测症状,通过F1分数来衡量性能.
主要成果:
- 患者的语音识别率为55.6%,明显低于整体率的76.1%.
- 症状检测的F1评分范围从0.31到0.46.
- 这项研究包括了平均年龄为80.6岁的患者.
结论:
- 语音识别和机器学习显示出在息治疗中自动化PROM评估的前景.
- 需要进一步改进模型以提高性能.
- 这项技术有可能减少供应商的负担,并增加PROM的利用率.
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