对住院患者的急性损伤预测模型偏移的分析和缓解
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
医疗保健中人工智能 (AI) 模型的持续监测和更新至关重要. 这项研究表明,适应性更新显著减少了急性损伤 (AKI) 预测模型中的性能漂移.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床决策支持 临床决策支持
- 机器学习在医学中的应用
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 增强了临床风险预测和诊断.
- 模型性能因人口变化和不断变化的实践导致数据漂移而恶化.
- 急性损伤 (AKI) 的预测模型容易随着时间的推移而导致性能下降.
研究的目的:
- 通过使用电子健康记录 (EHR) 调查AKI预测模型中的性能漂移.
- 评估模型更新策略在缓解绩效偏移方面的有效性.
- 分析不同患者子组的模型性能.
主要方法:
- 利用了10年来249,749名住院患者的EHR数据.
- 开发并应用了两个模型更新策略:整体人口更新 (OPU) 和特定子组更新 (SSU).
- 通过使用精度回忆曲线下的面积 (AUPRC) 评估模型性能,在总人口和九个不同的子组中进行评估.
主要成果:
- 无论是OPU还是SSU的策略都显著减少了绩效漂移.
- OPU改善了AUPRC的平均水平,整体为0.14,子组为0.11.
- 在子组中,SSU 改善了 AUPRC 的平均值 0.10.
结论:
- 在动态的临床环境中,持续的模型监测至关重要.
- 适应性更新策略 (OPU和SSU) 在保持可靠的预测性能方面是有效的.
- 需要定期重新校准模型以抵消数据漂移并确保临床实用性.
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