使用数字表型化数据预测精神分裂症患者的精神状态
Thierry Jean1,2, Rose Guay Hottin1, Pierre Orban1,2
1Research Center of the Montreal Mental Health University Institute, Montreal, Canada.
PLOS digital health
|February 7, 2025
概括
机器学习模型可以使用数字表型化数据预测心理状态. 计算数据不平衡的顺序回归模型,提供与二进制分类相比的性能,用于更丰富,可解释的预测.
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
- 精神病学数据分析
背景情况:
- 机器学习 (ML) 显示出预测精神病患者心理状态的前景.
- 以前的研究往往忽视了临床评级和数据不平衡的顺序性质.
研究的目的:
- 用数字表型化数据评估用于预测心理状态的ML算法.
- 为了比较顺序回归和二进制分类用于心理状态预测.
- 评估预测地平线和算法选择 (XGBoost与LSTM) 对预测性能的影响.
主要方法:
- 在从精神分裂症患者的CrossCheck数据集 (6,364次调查,23,551个传感器日) 上训练了120个ML模型.
- 使用XGBoost和LSTM算法利用顺序回归和二进制分类任务.
- 在同一天,第二天和下周的预测地平线上评估模型.
主要成果:
- 大多数模型的表现明显超过了基线,精度在58%-73%之间.
- 忽视数据不平衡的指标高估了业绩.
- XGBoost 模型的性能与 LSTM 模型相比或更好.
- 随着预测时间的延长,业绩略有下降.
结论:
- 使用适当的不平衡感知指标的顺序回归模型提供了临床上有价值和可解释的预测.
- 这些模型与二进制分类性能相匹配,而不会从自我报告中丢失信息.
- 数字表型结合适当的ML技术可以增强精神病学临床实践.
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