对同理心的算法:使用机器学习来对专业和同行对话中常见的同理心特征进行分类
Scott Provence1, Alyssa A Forcehimes2
1Founder, Versant Metrics, Duvall, USA.
Cureus
|May 7, 2024
概括
一个新的机器学习模型,Empathy算法,量化了对话中的同情心. 它确定了四个关键的同理心技能,在高质量的同理心互动中始终存在.
科学领域:
- 计算语言学 计算语言学
- 心理学 心理学 心理学
- 人工智能的人工智能
背景情况:
- 同理心在医疗保健和沟通中至关重要.
- 客观地衡量同情心是一个挑战.
- 机器学习为量化沟通技能提供了潜力.
研究的目的:
- 开发一个机器学习模型来识别和测量对话中的同情心.
- 为了比较算法的表现与人类评级的同情心在咨询和同行领导的对话.
- 确定特定的同理心技能是否可量化并与高质量的同理心互动相关.
主要方法:
- 开发了一种机器学习模型,结合了基于规则的和自然语言处理 (NLP) 的算法.
- 利用了三组专业咨询和同行主导对话的数据集,以同理心质量进行评分.
- 分析了使用"同理心算法"的对话,以检测六种预定义的同理心技能,并将它们的存在与高同理心评级相关联.
主要成果:
- 同理心算法显示,六个测量的同理心技能中的四个与高同理心对话之间存在正相关性.
- 确定了特定的,可量化的同理心技能,在有效沟通中始终存在.
- 展示了机器学习的潜力,以客观地评估同情沟通.
结论:
- 可量化的同理心技能存在于有效的对话中,并且可以通过机器学习来衡量.
- 使用算法对同理心的客观评估可以帮助改进沟通实践.
- 需要进一步开发以解决诸如非语言线索和细微语言等局限性.
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