用可穿戴设备和多实例学习，实时评估老年乳腺癌患者虚弱状态变化。
Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning
- 基于智能手表和心率变异性数据，构建多模态时间序列袋模型。
- 在独立患者测试中，手握力预测准确率达70%，生活质量评分达64%。
- 适合关注老年癌症患者长期康复监测的临床与研究者使用。
虚弱和功能衰退显著影响老年癌症患者的治疗耐受性和预后，但传统评估依赖有限的门诊检查。本研究提出一种多模态可穿戴设备框架，用于评估参与多中心CARDIOCARE研究的老年乳腺癌患者在随访间期的虚弱相关功能变化。通过智能手表提取自由生活状态下的运动与睡眠特征，结合胸带式设备获取的心电图衍生心率变异性（HRV）特征，按月3（M3）和月6（M6）随访时间点构建患者-时域袋。创新性地采用注意力机制的多实例学习（MIL）模型，融合存在缺失和弱监督条件下的异构、非规则多模态数据。该模型使用嵌入维度为128的模态专用多层感知机（MLP）编码器，聚合长度不一且部分缺失的纵向数据，以预测从基线起的功能变化类别（恶化、稳定、改善），对应于FACIT-F量表和手握力。在患者独立的留一患者排除（LOSO）评估中，全模态模型在M3和M6时的手握力预测平衡准确率/精确率为0.68±0.08/0.67±0.09和0.70±0.10/0.69±0.08；FACIT-F分别为0.59±0.04/0.58±0.06和0.64±0.05/0.63±0.07。消融实验表明，智能手表活动与睡眠信息提供最强预测能力，而HRV在融合后可提供互补信息。
原文摘要 · Abstract (English)
Frailty and functional decline strongly influence treatment tolerance and outcomes in older patients with cancer, yet assessment is typically limited to infrequent clinic visits. We propose a multimodal wearable framework to estimate frailty-related functional change between visits in elderly breast cancer patients enrolled in the multicenter CARDIOCARE study. Free-living smartwatch physical activity and sleep features are combined with ECG-derived heart rate variability (HRV) features from a chest strap and organized into patient-horizon bags aligned to month 3 (M3) and month 6 (M6) follow-ups. Our innovation is an attention-based multiple instance learning (MIL) formulation that fuses irregular, multimodal wearable instances under real-world missingness and weak supervision. An attention-based MIL model with modality-specific multilayer perceptron (MLP) encoders with embedding dimension 128 aggregates variable-length and partially missing longitudinal instances to predict discretized change-from-baseline classes (worsened, stable, improved) for FACIT-F and handgrip strength. Under subject-independent leave-one-subject-out (LOSO) evaluation, the full multimodal model achieved balanced accuracy/F1 of 0.68 +/- 0.08/0.67 +/- 0.09 at M3 and 0.70 +/- 0.10/0.69 +/- 0.08 at M6 for handgrip, and 0.59 +/- 0.04/0.58 +/- 0.06 at M3 and 0.64 +/- 0.05/0.63 +/- 0.07 at M6 for FACIT-F. Ablation results indicated that smartwatch activity and sleep provide the strongest predictive information for frailty-related functional changes, while HRV contributes complementary information when fused with smartwatch streams.
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