用语音分析预测心理热线来电优先级，准确率达92%
Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning
- 通过分析来电者语音特征，机器学习自动判断紧急程度
- 在459通电话数据上实现92%的平衡准确率
- 适合心理服务系统优化与智能辅助决策场景
确保准确的来电优先级划分对提升心理健康热线的效率和响应速度至关重要。目前，接线员完全依赖来电者的语言描述进行判断，纯主观评估易出错，且未利用通话中可获取的语音特征。错误分类可能导致高风险用户延误救助、资源错配、心理状况恶化、信任流失及法律风险。本研究探讨利用机器学习从语音中预测来电优先级的可行性。基于某心理健康热线的459条通话记录分析，模型实现92%的平衡准确率，显示出在提升接线效率与用户体验方面的潜力。
原文摘要 · Abstract (English)
Ensuring accurate call prioritisation is essential for optimising the efficiency and responsiveness of mental health helplines. Currently, call operators rely entirely on the caller's statements to determine the priority of the calls. It has been shown that entirely subjective assessment can lead to errors. Furthermore, it is a missed opportunity not to utilise the voice properties readily available during the call to aid in the evaluation. Incorrect prioritisation can result in delayed assistance for high-risk individuals, resource misallocation, increased mental health deterioration, loss of trust, and potential legal consequences. It is vital to address these risks to guarantee the reliability and effectiveness of mental health services. This study delves into the potential of using machine learning, a branch of Artificial Intelligence, to estimate call priority from the callers' voices for users of mental health phone helplines. After analysing 459 call records from a mental health helpline, we achieved a balanced accuracy of 92\%, showing promise in aiding the call operators' efficiency in call handling processes and improving customer satisfaction.
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