使用智能手机识别抑郁症的快速和最小系统:可解释的基于机器学习的方法
1Design Inclusion and Access Lab, North South University, Dhaka, Bangladesh.
JMIR formative research
|August 10, 2023
概括
这项研究开发了一种快速,极简的系统,使用应用程序使用数据在一秒钟内检测抑郁症. 机器学习模型实现了82.4%的准确性,为低资源设置提供了潜在的解决方案.
科学领域:
- 数字化表型化是指数字化表型化.
- 机器学习在心理健康中的应用
- 计算精神病学是一种计算精神病学.
背景情况:
- 现有的抑郁症检测系统需要随着时间的推移进行广泛的数据收集,这限制了它们在早期干预方面的有效性.
- 当前的方法可能是资源密集型的,在低资源环境中是不可行的.
研究的目的:
- 创建一个极简的系统,使用最少的数据快速识别抑郁症.
- 识别和解释最有效的机器学习模型来检测抑郁症.
主要方法:
- 开发了一种工具,可以在1秒内从100名孟加拉学生那里收集7天的应用使用数据.
- 采用各种机器学习模型 (线性,基于树的,神经网络) 具有特征选择 (过器,包装器,嵌入式) 和嵌套交叉验证.
- 使用Shapley添加式解释 (SHAP) 来实现模型的解释性.
主要成果:
- 一个增强光度梯度的机器模型在使用1秒应用程序使用数据识别抑郁的学生时达到82.4%的准确性.
- 采用~5个Boruta选择的特征的堆叠模型达到77.4%的精度和77.9%的平衡精度.
- 日常应用程序使用模式比汇总数据更能表明抑郁症;SHAP分析揭示了与抑郁症相关的特定行为标记.
结论:
- 这种快速,简单的系统在欠发达和发展中国家的抑郁症检测方面表现有前途.
- 这些发现可以指导开发更少的资源密集型系统,以了解和干预学生抑郁症.
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