用可穿戴设备视觉表示+多实例学习，预测老年乳腺癌患者心理压力。
Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning
- 将手环和心电贴片数据转为视觉图，用弱监督方式建模压力
- 3个月和6个月预测的R²达0.24和0.28，相关系数超0.49
- 适合关注老年肿瘤患者长期心理状态监测的研究者
心理压力在心血管肿瘤学中具有临床意义，但通常仅通过患者自评量表（PROMs）评估，很少融入持续的心脏毒性监测。本研究基于多中心老年乳腺癌队列（CARDIOCARE），利用智能手表（运动与睡眠）和胸戴式心电传感器的多模态可穿戴数据，将可穿戴信号转化为异构视觉表示，构建弱监督场景：单个感知压力量表（PSS）评分对应多个未标注时间窗。采用轻量级预训练专家混合模型（Tiny-BioMoE）将每张图像嵌入192维向量，再通过基于注意力的多实例学习（MIL）聚合特征，预测第3月（M3）和第6月（M6）的PSS分数。在留一患者外（LOSO）评估下，预测结果与问卷评分显示中等一致性：M3时R²=0.24，皮尔逊相关r=0.42，斯皮尔曼等级相关rho=0.48；M6时分别为0.28、0.49、0.52；全局RMSE/MAE在M3为6.62/6.07，在M6为6.13/5.54。
原文摘要 · Abstract (English)
Psychological stress is clinically relevant in cardio-oncology, yet it is typically assessed only through patient-reported outcome measures (PROMs) and is rarely integrated into continuous cardiotoxicity surveillance. We estimate perceived stress in an elderly, multicenter breast cancer cohort (CARDIOCARE) using multimodal wearable data from a smartwatch (physical activity and sleep) and a chest-worn ECG sensor. Wearable streams are transformed into heterogeneous visual representations, yielding a weakly supervised setting in which a single Perceived Stress Scale (PSS) score corresponds to many unlabeled windows. A lightweight pretrained mixture-of-experts backbone (Tiny-BioMoE) embeds each representation into 192-dimensional vectors, which are aggregated via attention-based multiple instance learning (MIL) to predict PSS at month 3 (M3) and month 6 (M6). Under leave-one-subject-out (LOSO) evaluation, predictions showed moderate agreement with questionnaire scores (M3: R^2=0.24, Pearson r=0.42, Spearman rho=0.48; M6: R^2=0.28, Pearson r=0.49, Spearman rho=0.52), with global RMSE/MAE of 6.62/6.07 at M3 and 6.13/5.54 at M6.
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