论文标题
不要忘记互动：使用逻辑回归预测199名患者的死亡
Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression
论文作者
论文摘要
在正在进行的COVID-19大流行中，是否可以在很大程度上恢复高风险的CoVID-19患者在很大程度上取决于如何在病毒对患者造成不可逆的后果之前适当地治疗他们的早期。在这项研究中，我们报告了一种基于逻辑回归的可解释，直观和准确的机器学习模型，以预测仅使用三种重要的血液生物标志物，包括乳酸脱氢酶，淋巴细胞（％）和高敏感性C-反应性C-反应性蛋白质及其相互作用的患者的死亡率。我们发现，当逻辑回归模型产生的死亡概率超过0.8时，该模型的最佳表现能够平均预测患者的死亡人数超过11.30天，并提前34.91天提前34.91天，累积的F1得分为93.76％，累积准确评分为93.92％。这样的模型可用于识别具有三种血液生物标志物高风险的Covid-19患者，并帮助世界各地的医疗系统计划在这一大流行中计划关键的医疗资源。
Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus. In this research, we reported an explainable, intuitive, and accurate machine learning model based on logistic regression to predict the fatality rate of COVID-19 patients using only three important blood biomarkers, including lactic dehydrogenase, lymphocyte (%) and high-sensitivity C-reactive protein, and their interactions. We found that when the fatality probability produced by the logistic regression model was over 0.8, the model had the optimal performance in that it was able to predict patient fatalities more than 11.30 days on average with maximally 34.91 days in advance, an accumulative f1-score of 93.76% and and an accumulative accuracy score of 93.92%. Such a model can be used to identify COVID-19 patients with high risks with three blood biomarkers and help the medical systems around the world plan critical medical resources amid this pandemic.
