Machine learning has demonstrated success in clinical risk prediction modeling with complex electronic health record (EHR) data. However, the evolving nature of clinical practices can dynamically change the underlying data distribution over time, leading to model performance drift. Adopting an outdated model is potentially risky and may result in unintentional losses. In this paper, we propose a novel Hybrid Adaptive Boosting approach (HA-Boost) for transfer learning. HA-Boost is characterized by the domain similarity-based and class imbalance-based adaptation mechanisms, which simultaneously address two critical limitations of the classical TrAdaBoost algorithm. We validated HA-Boost in predicting hospital-acquired acute kidney injury using real-world longitudinal EHRs data. The experiment results demonstrate that HA-Boost stably outperforms the competing baselines in terms of both Area Under Receiver Operating Characteristic and Area Under Precision-Recall Curve across a 7-year time span. This study has confirmed the effectiveness of transfer learning as a superior model updating approach in a dynamic environment.

中文翻译：

---

一种用于动态和不平衡数据实例迁移学习的混合自适应方法

机器学习已证明在使用复杂的电子健康记录 (EHR) 数据进行临床风险预测建模方面取得了成功。然而，临床实践的不断发展会随着时间的推移动态地改变底层数据分布，从而导致模型性能漂移。采用过时的模型具有潜在风险，并可能导致意外损失。在本文中，我们提出了一种新颖的混合A自适应B用于迁移学习的提升方法 (HA-Boost)。HA-Boost 的特点是基于域相似性和基于类不平衡的自适应机制，它同时解决了经典 TrAdaBoost 算法的两个关键限制。我们使用真实世界的纵向 EHR 数据验证了 HA-Boost 在预测医院获得性急性肾损伤方面的作用。实验结果表明，在 7 年的时间跨度内，HA-Boost 在接收器操作特征下的面积和精确召回曲线下的面积方面稳定优于竞争基线。这项研究证实了迁移学习在动态环境中作为一种高级模型更新方法的有效性。