Incremental domain adaptation-based ICU patient mortality prediction

Accurate prediction of ICU mortality based on physiological data can significantly facilitate timely interventions and improve patient outcomes. However, patient heterogeneity, including variations in age, disease types, and regional differences, along with the dynamic nature of ICU data, leads to significant limitations in the generalization ability and self-updating capability of existing prediction models. Therefore, developing dynamically adaptive predictive methods is crucial to ensure model adaptability and reliability in real-world clinical settings.

We propose AdaICU, a novel framework that integrates unsupervised domain adaptation and incremental learning for cross-ICU mortality prediction. The framework first aligns feature distributions between source and target domains using domain-adversarial training, and subsequently updates the model through incremental learning as new target-domain data become available.

Extensive experiments on the PhysioNet 2012 dataset show that AdaICU consistently outperforms baseline models across multiple cross-domain settings. In particular, AdaICU achieves AUROC improvements of approximately 2-6% over the strongest baseline across different transfer tasks. Substantial improvements are also observed in AUPRC, indicating enhanced capability in identifying high-risk patients. In addition, AdaICU improves accuracy by approximately 4-8% and achieves higher positive predictive value, suggesting more reliable positive predictions. Overall, these results highlight the effectiveness of the proposed framework in addressing cross-domain distribution shifts.

By jointly addressing cross-domain distribution shifts and post-deployment model adaptation, AdaICU provides a practical and effective framework for ICU mortality prediction in settings with limited labeled data. This capability makes it particularly promising for real-world application in dynamic clinical environments with evolving data distributions.