Zhao Xin, Nie Xiaokai
School of Mathematics, Southeast University, Nanjing 210096, China.
School of Automation, Southeast University, Nanjing 210096, China.
Entropy (Basel). 2022 Oct 17;24(10):1481. doi: 10.3390/e24101481.
In the status forecasting problem, classification models such as logistic regression with input variables such as physiological, diagnostic, and treatment variables are typical ways of modeling. However, the parameter value and model performance differ among individuals with different baseline information. To cope with these difficulties, a subgroup analysis is conducted, in which models' ANOVA and rpart are proposed to explore the influence of baseline information on the parameters and model performance. The results show that the logistic regression model achieves satisfactory performance, which is generally higher than 0.95 in AUC and around 0.9 in 1 and balanced accuracy. The subgroup analysis presents the prior parameter values for monitoring variables including SpO2, milrinone, non-opioid analgesics and dobutamine. The proposed method can be used to explore variables that are and are not medically related to the baseline variables.
在状态预测问题中，使用生理、诊断和治疗变量等输入变量的逻辑回归等分类模型是典型的建模方法。然而，具有不同基线信息的个体之间的参数值和模型性能存在差异。为了应对这些困难，进行了亚组分析，其中提出了模型的方差分析（ANOVA）和递归划分（rpart）来探索基线信息对参数和模型性能的影响。结果表明，逻辑回归模型取得了令人满意的性能，其曲线下面积（AUC）一般高于0.95，F1值和平衡准确率约为0.9。亚组分析给出了包括血氧饱和度（SpO2）、米力农、非阿片类镇痛药和多巴酚丁胺在内的监测变量的先验参数值。所提出的方法可用于探索与基线变量在医学上相关和不相关的变量。
