利用多模态可穿戴数据和多实例学习估计老年癌症患者的虚弱状态
Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning
AI总结：
本文提出一种多模态可穿戴框架，通过整合智能手表和心电图数据，利用多实例学习方法估计老年乳腺癌患者在随访期间的虚弱状态变化，实验结果显示模型在预测功能变化方面具有较高准确性。
摘要：
虚弱和功能下降显著影响老年癌症患者的治疗耐受性和预后，但评估通常局限于不频繁的门诊访问。我们提出了一种多模态可穿戴框架，用于估计老年乳腺癌患者在随访期间的虚弱相关功能变化。自由生活智能手表的活动和睡眠特征与通过心电图导出的心率变异性（HRV）特征结合，并整合到与第3月（M3）和第6月（M6）随访对齐的患者时间袋中。我们的创新是一个基于注意力的多实例学习（MIL）公式，它融合了不规则的多模态可穿戴实例，在现实世界中存在缺失和弱监督的情况下。一个基于注意力的MIL模型，具有模态特定的多层感知机（MLP）编码器，嵌入维度为128，将可变长度和部分缺失的纵向实例聚合起来，以预测FACIT-F和握力的离散化变化基线类别（恶化、稳定、改善）。在受试者独立的leave-one-subject-out（LOSO）评估下，完整的多模态模型在M3时手握力的平衡准确率/F1为0.68±0.08/0.67±0.09，在M6时为0.70±0.10/0.69±0.08，在M3时FACIT-F为0.59±0.04/0.58±0.06，在M6时为0.64±0.05/0.63±0.07。消融结果表明，智能手表的活动和睡眠提供了预测虚弱相关功能变化最强的信息，而HRV在与智能手表流融合时提供补充信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
