Wearable sensor data converted to visual embeddings and aggregated via attention MIL predicts perceived stress in elderly oncology patients with moderate accuracy (R² 0.24-0.28).
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Attention-based MIL fuses irregular smartwatch activity, sleep, and ECG HRV data to predict discretized changes in handgrip strength and FACIT-F with balanced accuracies of 0.59-0.70 under LOSO evaluation.
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Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning
Wearable sensor data converted to visual embeddings and aggregated via attention MIL predicts perceived stress in elderly oncology patients with moderate accuracy (R² 0.24-0.28).
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Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning
Attention-based MIL fuses irregular smartwatch activity, sleep, and ECG HRV data to predict discretized changes in handgrip strength and FACIT-F with balanced accuracies of 0.59-0.70 under LOSO evaluation.