University of Wisconsin, School of Medicine and Public Health.
Saint Louis University, School of Medicine.
AMIA Jt Summits Transl Sci Proc. 2021 May 17;2021:220-228. eCollection 2021.
Sepsis is a major cause of mortality in the intensive care units (ICUs). Early intervention of sepsis can improve clinical outcomes for sepsis patients. Machine learning models have been developed for clinical recognition of sepsis. A common assumption of supervised machine learning models is that the covariates in the testing data follow the same distributions as those in the training data. When this assumption is violated (e.g., there is covariate shift), models that performed well for training data could perform badly for testing data. Covariate shift happens when the relationships between covariates and the outcome stay the same, but the marginal distributions of the covariates differ among training and testing data. Covariate shift could make clinical risk prediction model nongeneralizable. In this study, we applied covariate shift corrections onto common machine learning models and have observed that these corrections can help the models be more generalizable under the occurrence of covariate shift when detecting the onset of sepsis.
败血症是重症监护病房（ICU）患者死亡的主要原因。早期干预败血症可以改善败血症患者的临床预后。已经开发了用于临床识别败血症的机器学习模型。有监督机器学习模型的一个常见假设是，测试数据中的协变量遵循与训练数据中相同的分布。当违反此假设（例如存在协变量偏移）时，在训练数据上表现良好的模型在测试数据上的表现可能会很差。当协变量与结果之间的关系保持不变，但训练数据和测试数据之间的协变量的边缘分布不同时，就会发生协变量偏移。协变量偏移会使临床风险预测模型不可推广。在这项研究中，我们将协变量偏移校正应用于常见的机器学习模型，并观察到这些校正可以帮助模型在发生协变量偏移时更具通用性，从而检测败血症的发生。
