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 Adopting an outdated model is potentially risky and may result in unintentional losses.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：

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一种混合自适应方法，用于动态和不平衡数据的实例迁移学习


机器学习已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可能会随着时间的推移动态改变底层数据分布，从而导致模型性能漂移。采用过时的模型存在潜在风险，并可能导致意外损失。在本文中，我们提出了一种用于迁移学习的新型混合自适应增强方法（HA-Boost）。 HA-Boost 的特点是基于领域相似性和基于类不平衡的适应机制，它同时解决了经典 TrAdaBoost 算法的两个关键局限性。我们使用真实世界的纵向 EHR 数据验证了 HA-Boost 预测医院获得性急性肾损伤的能力。实验结果表明，在 7 年的时间跨度内，HA-Boost 在接收器操作特性下面积和精确召回曲线下面积方面均稳定优于竞争基线。这项研究证实了迁移学习作为动态环境中优越的模型更新方法的有效性。