A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data

Abstract

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 Adopting an outdated model is potentially risky and may result in unintentional losses.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