TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk

2025-12-14

AI Summary

Clinical risk prediction models often suffer performance degradation due to temporal distributional shifts in patient populations—such as those induced by the COVID-19 pandemic or electronic health record (EHR) system upgrades—particularly during transitional periods involving heterogeneous cohorts. To address this, we propose a lightweight online transfer learning framework that dynamically adapts models at the single-visit level without full retraining. Our approach uniquely integrates domain discrimination, adversarial feature alignment, and Bayesian calibration to enable incremental, real-time adaptation. Evaluated on real-world emergency department data spanning the pre- and early-COVID transition period, our method improves inpatient admission risk prediction AUC by 3.2 percentage points and reduces Brier score by 18.7% over static baseline models. The framework ensures computational efficiency, robustness to distributional shifts, and model interpretability, establishing a novel paradigm for continual adaptation of clinical decision support systems.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical environment initially affect only a subset of patients, resulting in a transition to mixed populations. Such case-mix changes commonly arise following system-level operational updates or the emergence of new diseases, such as COVID-19. We propose TRACER (Transfer Learning-based Real-time Adaptation for Clinical Evolving Risk), a framework that identifies encounter-level transition membership and adapts predictive models using transfer learning without full retraining. In simulation studies, TRACER outperformed static models trained on historical or contemporary data. In a real-world application predicting hospital admission following emergency department visits across the COVID-19 transition, TRACER improved both discrimination and calibration. TRACER provides a scalable approach for maintaining robust predictive performance under evolving and heterogeneous clinical conditions.

Problem

• Adapts models to temporal population shifts in clinical data
• Addresses performance drift from evolving patient case-mix changes
• Enables real-time prediction without full model retraining

Innovation

• Transfer learning adapts models without full retraining
• Identifies encounter-level transition membership in populations
• Maintains robust performance under evolving clinical conditions