语音作为数字生物标志物的计算分析,用于临床评估女性癌症患者的痛苦
概括
数字语音生物标志物显示出对女性癌症患者的痛苦监测有前途. 声学语音特征与困境严重程度相关,使客观,非侵入性查和改善息治疗成为可能.
科学领域:
- 数字健康数字健康
- 生物医学工程 生物医学工程
- 在瘤学瘤学.
背景情况:
- 癌症患者的息护理优先考虑症状减轻和应急缓解.
- 数字生物标志物提供非侵入性健康监测解决方案.
- 声学语音分析是一种新兴的数字生物标志物模式.
研究的目的:
- 研究女性癌症患者声学语音特征与困境严重程度之间的相关性.
- 识别基于语音的生物标志物,用于客观的危险评估.
- 探索语音分析在息性瘤学的临床相关性.
主要方法:
- 在多个时间点收集了28名女性癌症患者的语音录音.
- 使用openSMILE工具包 (ComParE 2016功能集) 提取的声学特征.
- 专注分析了Mel过器银行带 (MFB 23,MFB 24) 并使用了皮尔森相关性和OLS回归.
主要成果:
- 确定了13种与危险分数 (埃德蒙顿症状评估系统 (ESAS)) 有关的光谱特征.
- 多变量回归显示,声学参数解释了29%的应急变异.
- 在MFB 23中,光谱平度和平均能量是危险的关键预测因素.
结论:
- 语音衍生的数字生物标志物可以促进瘤学中的自动化,客观的应急查.
- 将语音分析集成到临床工作流程中,支持持续的症状监测和早期干预.
- 这些发现突出了加强远程医疗和在息治疗中精确症状管理的潜力.
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