预测模型的精度的种族差异高流量鼻管在COVID-19失败预测模型的精度
Philip Yang1, Ismail A Gregory1, Chad Robichaux2
1Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA.
Critical care explorations
|July 8, 2024
概括
机器学习模型可以预测COVID-19患者的高流鼻腔管失效,超过ROX指数. 然而,XGB模型显示了种族差异的准确性,需要进一步研究偏见缓解.
科学领域:
- 临界护理医学 临界护理医学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 高流量鼻腔管 (HFNC) 治疗对于管理COVID-19呼吸衰竭至关重要.
- 需要预测模型来早期识别有HFNC失效风险的患者.
- 像ROX指数这样的现有方法在准确性上有局限性.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测COVID-19患者的HFNC失败.
- 将ML模型的性能与ROX指数进行比较.
- 评估不同种族群体的ML模型准确性.
主要方法:
- 成年COVID-19患者接受HFNC治疗的回顾性队列研究.
- 开发四个监督的ML模型,使用从ICU入院前24小时起的临床变量.
- 在单独的队列上对模型的验证,以模拟未来的使用.
主要成果:
- 与ROX指数相比,Extreme Gradient Boosting (XGB) 模型在预测HFNC故障 (输管或7天内死亡) 中表现优异.
- 在XGB模型的AUROC达到0.707,显著超过ROX指数 (AUROC0.616).
- 观察到显著的种族差异,XGB模型在黑人患者中表现较差,与白人患者相比.
结论:
- 开发的XGB模型显示了预测HFNC在COVID-19中的失败的前景,但显示了种族偏见.
- 进一步的研究是必要的,以了解和解决临床ML模型中的偏差,以实现公平的应用.
- 需要缓解策略来确保公平性,并提高基于ML的医疗预测的公平性.
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