预测心理治疗中的工作联盟:一种多模式机器学习方法
Katie Aafjes-van Doorn1,2, Marcelo Cicconet1, Jeffrey F Cohn1,3
1Deliberate AI, New York, NY, United States.
概括
现在可以使用来自视频录制的多式联络行为数据在治疗会上自动预测工作联盟. 这种方法准确地识别了联盟问题,有可能改善治疗结果并减少患者退学.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 治疗联盟对于治疗成功至关重要,但其一致的监测是具有挑战性的.
- 工作联盟的传统自我报告措施缺乏广泛的临床实施.
- 联盟评估的自动化方法可以促进及时干预.
研究的目的:
- 开发一种自动化系统,用于预测治疗会话中的工作联盟.
- 利用视频录制的会议的行为特征来预测联盟.
- 调查多式联机机器学习模型对此任务的有效性.
主要方法:
- 分析了252个治疗会话 (面对面和远程治疗) 的数据集,与患者评级的工作联盟进行了分析.
- 从会议录音中提取了文字,音频和视频功能.
- 机器学习回归模型,包括梯度提升,使用融合多式联运特征进行训练.
主要成果:
- 渐变增强模型使用患者的音频,文本和视频特征实现了最高的准确性 (ICC=0.66,Pearson r=0.70,MAE=0.33).
- 多模式功能融合显著提高了预测性能.
- 在这项研究中,自动化联盟预测显示了高准确度.
结论:
- 从视频录制的治疗课程中进行自动化联盟预测是一种可行和准确的方法.
- 数据驱动的多式联运特征提取和选择产生了强大的预测模型.
- 这项技术有可能通过早期检测联盟问题来改善临床实践.
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