用统一指标捕捉昼夜节律，实现抑郁筛查与干预建议的可解释分析
Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
- 提出昼夜节律得分（CRS），融合多维度行为数据压缩为单一指标
- 在CHARLS数据上达到AUC 0.825，识别出有效运动量300 MET-min/周等阈值
- 支持个性化干预推断，适合临床研究与健康数据分析者使用
从大规模行为数据中进行抑郁症筛查面临昼夜节律指标碎片化、可解释性差及缺乏干预导向分析的挑战。现有方法通常孤立分析睡眠、活动与社交行为，未能捕捉其联合的昼夜结构。为此，我们提出昼夜节律得分（CRS），一个将多领域日间行为压缩为统一昼夜节律表征的复合指数。CRS通过非负性约束保留行为语义的同时，最大化对抑郁症的区分能力。实证结果表明，单个CRS可近乎无损地保留原始行为指标的预测能力。基于CRS，我们构建了基于梯度提升树与SHAP分析的可解释筛查框架，揭示了昼夜节律与抑郁风险间的非线性及饱和关系。进一步整合交互建模与反事实回归，可估计异质性且剂量依赖的行为效应，支持不同昼夜情境下的干预推理。在包含15,233名个体的中国健康与退休纵向研究（CHARLS）数据上，模型表现稳健（ROC-AUC=0.825），识别出最小有效运动量约300 MET-min/周，以及对睡眠不足者最优的恢复性小憩时长约65分钟。本工作通过连接监督表示学习与可解释建模，为抑郁症筛查与干预感知的医疗数据挖掘提供可扩展框架。
原文摘要 · Abstract (English)
Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented analysis. Existing approaches typically analyze sleep, activity, and social behaviors in isolation, failing to capture their joint circadian structure. To address this limitation, we first propose the Circadian Rhythm Score (CRS), a composite index that compresses multi-domain daily behaviors into a unified representation of circadian rhythm. CRS is constructed to maximize discriminative power for depression screening while preserving behavioral semantics through non-negativity constraints. Empirical results demonstrate near-lossless compression, where a single CRS retains almost the full predictive capability compared with multiple raw behavioral indicators. Building upon CRS, we develop an interpretable depression screening framework based on gradient-boosted trees and SHAP analysis, revealing nonlinear and saturation-like associations between circadian rhythm and depression risk. Beyond risk prediction, we further integrate interaction modeling and counterfactual regression to estimate heterogeneous and dose-dependent behavioral effects, enabling intervention-oriented reasoning under different circadian contexts. Experiments on the China Health and Retirement Longitudinal Study (CHARLS, n=15,233), demonstrate robust screening performance (ROC-AUC=0.825) and identify actionable behavioral thresholds, including a minimum effective exercise dose of approximately 300 MET-min/week and an optimal restorative nap duration of approximately 65 minutes for sleep-deprived individuals. By bridging supervised representation learning and interpretable modeling, this work provides a scalable framework for depression screening and intervention-aware healthcare data mining.
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