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.
基于视觉可穿戴表征与多示例学习的老年肿瘤患者压力评估 / Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning
本研究提出了一种新方法，通过智能手表和心电传感器采集老年人的活动与睡眠数据，将其转换为视觉图像，并利用一个轻量级AI模型来预测癌症患者的心理压力水平，从而为持续监测心脏毒性风险提供了客观、便捷的工具。
源自 arXiv: 2604.06990
