用注意力GRU自编码器分析可穿戴设备数据，自动识别抑郁行为亚型。
AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data
- 融合重建、分类与软聚类的多任务模型，学习行为时序特征
- 聚类质量（轮廓系数0.70）和抑郁预测（AUC 0.74）均优于基线
- 可解释时间注意力，揭示睡眠规律变化的关键时段
本研究提出AttentiveGRUAE，一种基于注意力机制的门控循环单元自编码器，用于从纵向可穿戴设备数据中进行时序聚类与结局预测。模型联合优化三项目标：(1) 通过序列重构学习每日行为特征的紧凑潜在表示；(2) 通过二分类头预测周期末期抑郁率；(3) 基于高斯混合模型对学习到的嵌入进行软聚类，识别行为亚型。在372名参与者（GLOBEM 2018-2019）的纵向睡眠数据上评估，其聚类质量（轮廓系数=0.70）和抑郁分类性能（AUC=0.74）均显著优于基线、领域对齐自监督及消融模型（聚类轮廓系数0.32–0.70，AUC 0.50–0.67）。跨年队列（332名参与者，GLOBEM 2020–2021）外部验证确认了聚类可复现性（轮廓系数0.63，AUC 0.61）与稳定性。亚型分析与时间注意力可视化揭示了各聚类间睡眠特征差异，并定位与睡眠规律变化吻合的关键时间窗口，提供临床可解释的风险表征。
原文摘要 · Abstract (English)
In this study, we present AttentiveGRUAE, a novel attention-based gated recurrent unit (GRU) autoencoder designed for temporal clustering and prediction of outcome from longitudinal wearable data. Our model jointly optimizes three objectives: (1) learning a compact latent representation of daily behavioral features via sequence reconstruction, (2) predicting end-of-period depression rate through a binary classification head, and (3) identifying behavioral subtypes through Gaussian Mixture Model (GMM) based soft clustering of learned embeddings. We evaluate AttentiveGRUAE on longitudinal sleep data from 372 participants (GLOBEM 2018-2019), and it demonstrates superior performance over baseline clustering, domain-aligned self-supervised, and ablated models in both clustering quality (silhouette score = 0.70 vs 0.32-0.70) and depression classification (AUC = 0.74 vs 0.50-0.67). Additionally, external validation on cross-year cohorts from 332 participants (GLOBEM 2020-2021) confirms cluster reproducibility (silhouette score = 0.63, AUC = 0.61) and stability. We further perform subtype analysis and visualize temporal attention, which highlights sleep-related differences between clusters and identifies salient time windows that align with changes in sleep regularity, yielding clinically interpretable explanations of risk.
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