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Wearable data, particularly smartwatch activity and sleep features, can predict frailty-related functional changes in elderly cancer patients with surprisingly high accuracy, offering a more continuous and objective assessment than infrequent clinic visits.
Wearable sensor data, transformed into visual representations and processed with attention-based MIL, can predict perceived stress in elderly cancer patients with moderate accuracy, offering a less intrusive alternative to questionnaires.
