Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning

• URL: http://arxiv.org/abs/2604.06990v1
• Date: Wed, 08 Apr 2026 12:06:21 GMT
• Title: Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning
• Abstract summary: We estimate perceived stress in an elderly, multicenter breast cancer cohort using multimodal wearable data.<n>A single Perceived Stress Scale (PSS) score corresponds to many unlabeled windows.<n>A lightweight pretrained mixture-of-experts backbone embeds each representation into 192-dimensional vectors, which are aggregated via attention-based multiple instance learning.
• Score: 1.226215564499691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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