预测抑郁症严重程度和自杀风险的多式多任务学习使用预训练的音频和文本嵌入:方法的开发和应用
Ya-Han Hu1,2, Ruei-Yan Wu1,3, Min-Yi Su1
1Department of Information Management, National Central University, No. 300, Zhongda Rd., Zhongli Dist., Taoyuan City, Taiwan.
JMIR medical informatics
|October 30, 2025
概括
这项研究表明,结合音频和文本数据的多任务学习模型可以改善抑郁症严重程度和自杀风险分类. 这些深度学习模型为临床决策支持提供了有希望的客观方法.
科学领域:
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
- 深度学习用于心理健康评估
背景情况:
- 抑郁症的严重程度和自杀风险需要及时评估和治疗.
- 准确识别抑郁症严重程度 (DS) 和自杀风险 (SR) 对于有效管理至关重要.
- 现有的机器学习和深度学习研究在同时解决DS和SR方面存在局限性.
研究的目的:
- 评估集成多任务学习 (MTL),多模式学习和转移学习的深度学习模型.
- 提高联合分类对抑郁症严重程度和自杀风险的有效性.
- 用预训练嵌入器评估音频和文本数据的联合性能.
主要方法:
- 提出了一个多任务框架,使用预训练的音频和文本嵌入的多式融合.
- 数据包括中国语录音和200名参与者的临床问卷分数.
- 与单任务学习 (STL) 模型相比,使用连接和硬参数共享集成了预训练的嵌入.
主要成果:
- 单任务学习模型在DS (AUC=0.878) 和SR (AUC=0.876) 预测方面取得了高性能.
- 多任务学习模型显著改善了SR预测,而不是DS预测.
- 在MTL模型中,获得了最高的DS分类 (AUC=0.887) 和SR分类 (AUC=0.883).
结论:
- 拟议的MTL模型有效地提高了抑郁症严重程度和自杀风险分类,使用特定的音频和文本嵌入.
- 在MTL实施期间建议小心,以减轻潜在的负面转移影响.
- 这项研究提供了一种有希望的客观方法,用于临床决策支持并行DS和SR诊断.
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