对于分类模型的最有效干预措施 基于在线自杀预防聊天中的对话内容来预测聊天结果的模型开发:机器学习方法
Salim Salmi1, Saskia Mérelle1, Renske Gilissen1
1Research Department, 113 Suicide Prevention, Amsterdam, Netherlands.
JMIR mental health
|September 26, 2024
概括
对自杀预防热线聊天的机器学习分析显示,积极的肯定和帮助者的参与改善了寻求帮助者的结果. 相反,过早的聊天结束和自动响应会对用户产生负面影响.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 自杀预防热线的最佳护理需要了解影响求助者的结果的因素.
- 基于文本的聊天服务产生大量数据用于大规模分析.
研究的目的:
- 训练一个机器学习模型来预测自杀预防热线聊天结果.
- 识别特定的辅导员发言影响模型预测,并帮助寻求者得分.
主要方法:
- 从6903名求助者的聊天对话中训练了一种机器学习分类模型 (2021年8月至2023年1月).
- 利用机器学习的文本分析来预测帮助寻求者在自杀因素 (例如,绝望,生活意愿) 上的得分.
- 采用两种解释方法,在聊天数据中识别有影响力的助手消息.
主要成果:
- 帮助者的积极肯定和参与表达与帮助寻求者得分的改善正相关.
- 使用自动响应 (宏) 和过早结束聊天对帮助寻求者的结果产生了负面影响.
- 机器学习模型成功地根据对话内容预测了聊天结果.
结论:
- 洞察力建议通过一种富有吸引力的风格来改善帮助电话聊天,包括问题,肯定和实用建议.
- 机器学习显示了分析帮助电话聊天数据以增强支持的巨大潜力.
- 了解特定的沟通元素可以优化危机中的个人护理.
关键词:
贝尔特 (BERT) 公司在法学士 (LLM) 课程中.人工智能的人工智能是人工智能.来自变压器的双向编码器表示这是分类分类的分类.谈话 会话 会话 会话可以解释的人工智能AI帮助电话 帮助电话可解释的人工智能AI大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.自杀倾向 自杀倾向 自杀倾向 自杀倾向自杀 自杀 自杀 自杀 自杀 自杀 自杀自杀辅助电话 自杀辅助电话自杀预防 自杀预防更多相关视频
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