自动分类暴露和鼓励事件的语音数据从儿科强迫症治疗的儿童
Juan Antonio Lossio-Ventura1, Samuel Frank2, Grace Ringlein2
1Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States.
使用语音识别和自然语言处理的自动化系统现在可以编码暴露治疗会话. 这项技术有助于实时临床评估和研究有效治疗方法.
科学领域:
- 医疗保健中的人工智能
- 临床心理学 临床心理学
- 自然语言处理自然语言处理.
背景情况:
- 暴露疗法是强迫症 (OCD) 的关键治疗方法之一.
- 精确编码治疗会话对于质量评估和研究至关重要.
- 手动编码耗时且容易出现错误.
研究的目的:
- 开发和评估一个自动化系统来对暴露过程编码系统 (EPCS) 质量代码进行分类.
- 该系统专注于识别儿童强迫症治疗课程中的暴露和鼓励事件.
- 使用自动语音识别 (ASR) 和自然语言处理 (NLP) 技术.
主要方法:
- 在360个手动标记的儿科强迫症治疗课程中接受了培训和测试.
- 使用ASR工具 (Whisper,谷歌语音到文本) 转录的音频录音.
- 用变压器模型 (BERT, Sentence-BERT, Llama 3) 分析成绩单,用于序列和令牌级别的分类.
主要成果:
- 威斯珀实现了比谷歌语音到文本 (WER 0.51) 更高的转录精度 (WER 0.31).
- 拉玛3模型在序列分类方面表现出色 (曝光时AUC为0.95).
- 微调的BERT模型在代币级别分类中表现最好 (风险投资的AUC为0.85).
结论:
- ASR和变压器模型可以自动化曝光疗法会话的质量编码.
- 这使得实时评估在临床实践和可扩展的研究.
- 未来的工作包括改进ASR,扩大数据集和整合多式联运数据.
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