基于机器学习的自杀风险评估在危机辅导中的评估
Zac E Imel1, Brian Pace1, Brad Pendergraft1
1Lyssn.io, Seattle (Imel, Pace, Pruett, Tanana, Soma, Atkins); Protocall Services, Portland, Oregon (Pendergraft); Harborview Medical Center, University of Washington, Seattle (Comtois).
Psychiatric services (Washington, D.C.)
|July 19, 2024
概括
机器学习模型现在可以在危机咨询呼叫中自动检测自杀风险评估. 这项技术为改善危机干预服务质量提供了一个可扩展的解决方案.
科学领域:
- 人工智能的人工智能
- 心理健康技术 心理健康技术
- 临床心理学 临床心理学
背景情况:
- 自杀风险评估在危机咨询中至关重要,这是标准要求的.
- 目前的质量改进依赖于人工,缓慢和不可扩展的人类对对话的评估.
- 人类评估的局限性阻碍了增加风险评估频率的努力.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于在危机咨询中自动检测自杀风险评估.
- 提供可扩展的工具,以提高危机干预的质量.
主要方法:
- 476个危机咨询电话 (193,257个陈述) 的数据集被手动编码为风险评估元素.
- 基于变压器的ML模型使用这些标记数据进行了微调.
- 该模型使用单独的培训,验证和测试数据集进行了评估.
主要成果:
- ML模型与人类评分器具有很高的一致性,与人类评分器之间的可靠性达成98%的一致性,用于检测任何风险评估.
- 该模型在呼叫级别达到0.86的平均F1分,在声明级别达到0.66.
- 由于基准利率较低,对特定标签的表现有所不同.
结论:
- 机器学习模型可以在危机咨询对话中可靠地识别自杀风险评估.
- 这项技术为扩大心理健康危机服务质量改善举措提供了重要机会.
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