Personalized Digital Health Modeling with Adaptive Support Users
This work addresses the problem of personalization in digital health, where user-specific data is scarce and noisy, by improving model accuracy and data efficiency.
The paper proposes a unified personalization framework for digital health that trains personal models using adaptively weighted support users, including both similar and dissimilar individuals. It achieves up to 10% lower RMSE on large-scale datasets and 25% lower RMSE in low-data settings.
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. An iterative optimization algorithm jointly updates model parameters and user similarity weights. Experiments on six tasks across four real-world digital health datasets show consistent improvements over population and personalized baselines. The method achieves up to 10% lower RMSE on large-scale datasets and approximately 25% lower RMSE in low-data settings. The learned adaptive weights improve data efficiency and provide interpretable guidance for targeted data selection.