DeepGAM:使用通用添加模型进行抑郁症诊断的可解释深度神经网络:来自心灵和灵魂的数据研究
Chiyoung Lee1, Yeri Kim2, Seoyoung Kim2
1The University of Arizona College of Nursing, Tucson, Arizona, United States of America.
PloS one
|September 5, 2025
概括
我们开发了DeepGAM, 一种可解释抑郁症诊断的新型深度学习模型. 它使用较少的特征准确预测抑郁症状况,
科学领域:
- 人工智能
- 机器学习
- 计算精神病学
背景情况:
- 深度神经网络在各种任务中表现出色,但往往缺乏解释性,阻碍了临床应用.
- 从个人医疗数据中估计抑郁症状况正在获得吸引力, 但理解模型决策仍然具有挑战性.
- 现有的黑盒模型使得难以辨别输入特征对抑郁症诊断的影响.
研究的目的:
- 引入基于深度学习的通用添加模型DeepGAM,提高抑郁症诊断的可解释性.
- 开发一个可以识别单个组件对抑郁状态的正面和负面影响的模型.
- 在心理健康预测的背景下改善特征选择和模型解释性.
主要方法:
- 提出了DeepGAM,一种基于神经网络的附加函数的深度学习通用附加模型.
- 为受约束,规范和可解释的输出设计了网络架构和目标功能.
- 使用直通估计器 (STE) 通过梯度下降来进行特征选择,从而实现更少特征的性能.
主要成果:
- DeepGAM获得了最高的AUC (0.600) 和F1得分 (0.387),超过了传统的神经网络和IGANN.
- STE选择了与99个功能相匹配的5个关键功能, 优于Lasso和Boruta.
- 展示了DeepGAM在公共数据集上的可解释性和强大性能.
结论:
- 提供精确可解释的抑郁症预测方法.
- 该模型提供了对个体特征贡献的见解,解决了深度学习的黑子性质.
- 这种方法通过将预测能力与可解释性结合起来, 推动机器学习在心理健康领域的应用.
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