Personalized Digital Health Modeling with Adaptive Support Users
Personalized models are essential in digital health because individuals exhibit substantial physiological and behavioral heterogeneity. Yet personalization is limited by scarce and noisy user-specific data. Most existing methods rely on population pretraining or data from similar users only, which can lead to biased transfer and weak generalization. We propose a unified personalization framework that trains a personal model using adaptively weighted support users, including both similar and dissimilar individuals. The objective integrates personal loss, similarity-weighted transfer from similar users, and contrastive regularization from dissimilar users to suppress misleading correlations...
| Authors | Zhongqi Yang, Mahkameh Rasouli, Neda Mohseni et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 May 2026 |
| Last updated at source | 14 May 2026 |
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