人工智能预测模型在医疗保健环境中的应用:一项模拟研究
Akhil Vaid1, Ashwin Sawant2, Mayte Suarez-Farinas3
1Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.).
Annals of internal medicine
|October 9, 2023
概括
在医疗保健中实施预测模型可以随着时间的推移降低他们的表现. 持续监测和记录模型使用对于保持临床环境中的预测准确性至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 医疗保健中的机器学习
背景情况:
- 预测模型在医疗保健中越来越多地使用,但它们在现实世界中的实施可能会影响患者的结果并改变电子健康记录数据.
- 这种数据变化反过来又会影响当前和未来预测模型的性能.
研究的目的:
- 评估预测模型在临床实践中实施后的性能如何变化.
- 检查三个常见的场景:模型再培训,连续的模型实施和与干预同时实施.
主要方法:
- 在重症监护机构模拟了预测模型的实施和使用.
- 测试了不同级别的临床执法和干预效果.
- 模型在模拟后被训练或重新训练.
主要成果:
- 重新训练一个死亡率预测模型导致特异性减少了9%-39%.
- 在死亡率模型之前实施急性损伤 (AKI) 模型将后者的特异性降低了8%-15%.
- 同时实施AKI和死亡率模型将对方的准确性降低了1% - 28%.
结论:
- 没有单一的模型更新策略可以保证在模拟ICU设置中持续的性能.
- 记录预测模型的使用可能对于它们的持续可行性和准确性至关重要.
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