通过自适应权重融合相似与不相似用户数据，提升数字健康个性化建模效果。
Personalized Digital Health Modeling with Adaptive Support Users
- 根据用户相似度动态加权支持用户，构建统一个性化框架。
- 在低数据场景下降低约25%的均方根误差，大规模数据上降10%。
- 自学习权重可解释，帮助选择关键数据，适合数据稀缺场景使用。
个性化模型在数字健康中至关重要，因个体间存在显著的生理与行为差异。然而，个性化受限于稀缺且嘈杂的用户特定数据。现有方法多依赖群体预训练或相似用户数据，易导致迁移偏差和泛化能力弱。本文提出一种统一的个性化框架，利用自适应加权的支持用户（包括相似与不相似个体）训练个人模型。目标函数整合个人损失、相似用户加权迁移项及不相似用户对比正则化，以抑制误导性关联。采用迭代优化算法联合更新模型参数与用户相似度权重。在四个真实数字健康数据集上的六项任务实验表明，该方法持续优于群体和个性化基线。在大规模数据集上，均方根误差（RMSE）最高降低10%；在低数据场景下，约降低25%。学习到的自适应权重提升了数据效率，并为精准数据选择提供可解释指导。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。