面向可解释的人工智能 (XAI) 基于语言行为来进行心理健康检测
Elma Kerz1, Sourabh Zanwar1, Yu Qiao1
1Department of English and American Studies, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany.
Frontiers in psychiatry
|December 25, 2023
概括
可解释的人工智能 (XAI) 方法提高了自动化从社交媒体文本中检测心理健康障碍的透明度. 本研究使用BiLSTM和变压器模型与先进的特征工程和解释技术平衡了预测准确性和可解释性.
科学领域:
- 计算语言学计算语言学
- 人工智能的人工智能
- 心理健康信息学心理健康信息学
背景情况:
- 深度学习模型在心理健康预测中优先考虑预测准确性而不是可解释性.
- 在医疗保健应用中,人工智能决策缺乏透明度至关重要.
- 可解释性AI (XAI) 是可靠的精神病诊断和预测所需要的.
研究的目的:
- 从社交媒体语言系统地研究XAI自动化精神障碍检测方法.
- 评估心理健康预测模型中准确性和可解释性之间的权衡.
- 识别特定心理健康状况的信息性语言特征.
主要方法:
- 使用人类可解释的特征 (语法,词汇,可读性,凝聚力,风格,主题,情感/情感) 构建BiLSTM模型.
- 比较BiLSTM模型与适应心理健康的黑盒式变压器模型.
- 使用多任务融合学习 (情感,人格特征) 和解释技术 (LIME,AGRAD) 增强了变压器的解释能力.
主要成果:
- 广泛的实验评估了跨模型的准确性-解释性平衡.
- 特征剥离识别了特定心理健康状况的关键语言特征.
- XAI技术 (LIME,AGRAD) 揭示了影响变压器预测的词类别.
结论:
- XAI方法为心理健康预测提供了黑子模型的透明替代方案.
- 将可解释的特征与先进的AI技术相结合,可以增强模型的理解.
- 这项研究为在心理健康信息学中开发可解释的人工智能提供了框架.
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