使用机器学习进行心理健康预测和呼叫优先预测的可行性
Rajib Rana1, Niall Higgins1,2, Kazi Nazmul Haque1,3
1School of Mathematics, Physics and Computing, Springfield Campus, University of Southern Queensland, Springfield Education City, QLD 4300, Australia.
Nursing reports (Pavia, Italy)
|December 27, 2024
概括
机器学习模型现在可以分析语音模式,以准确评估心理健康救援电话的呼叫优先级. 这种人工智能驱动的方法提高了效率,并确保对高危呼叫者的及时干预.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 语音信号处理 语音信号处理
背景情况:
- 准确的呼叫优先级对心理健康帮助电话效率和呼叫者结果至关重要.
- 目前的分组依赖于主观的临床判断,需要客观的方法.
- 及时识别高危呼叫者至关重要,因为精神疾病的发病率和死亡率很高.
研究的目的:
- 调查机器学习 (ML) 的使用,以估计心理健康热线的呼叫优先级.
- 分析语音属性,而不是口语内容,用于呼叫评估.
主要方法:
- 来自电话呼叫者的语音数据使用现有的API进行处理.
- 从原始音频中提取特征,并输入深度学习神经网络.
- 开发了一个分类模型,从音频表示中确定呼叫优先级.
主要成果:
- 开发了一个深度学习神经网络架构,用于基于语音的即时优先级评估.
- 该模型分析了459个心理健康热线呼叫记录.
- 最终的ML模型在识别呼叫优先级方面实现了92%的平衡精度.
结论:
- 开发的ML模型提供了基于语音质量的客观测量呼叫优先级.
- 结果表明,语音分析可以估计呼叫者的行为 (积极/消极).
- 这个优先级可以通过网页界面显示,以便实时提供决策支持.
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