对抗性漂移感知的预测迁移：迈向持久的临床AI
Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI
AI总结：
ADAPT通过最小化年度性能下降，无需重新训练或标记数据，提供了一种在医疗领域维持可靠AI的可扩展方法。
摘要：
临床AI系统在部署后经常因时间数据漂移而出现性能下降，例如人口变化、诊断编码更新（如ICD-9到ICD-10）以及系统性冲击如新冠疫情。通过频繁重新训练来解决这种
英文摘要：
Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing this ``aging'' effect via frequent retraining is often impractical due to computational costs and privacy constraints. To overcome these hurdles, we introduce Adversarial Drift-Aware Predictive Transfer (ADAPT), a novel framework designed to confer durability against temporal drift with minimal retraining. ADAPT innovatively constructs an uncertainty set of plausible future models by combining historical source models and limited current data. By optimizing worst-case performance over this set, it balances current accuracy with robustness against degradation due to future drifts. Crucially, ADAPT requires only summary-level model estimators from historical periods, preserving data privacy and ensuring operational simplicity. Validated on longitudinal suicide risk prediction using electronic health records from Mass General Brigham (2005--2021) and Duke University Health Systems, ADAPT demonstrated superior stability across coding transitions and pandemic-induced shifts. By minimizing annual performance decay without labeling or retraining future data, ADAPT offers a scalable pathway for sustaining reliable AI in high-stakes healthcare environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
