标题： 不要忘记交互作用：使用逻辑回归预测新冠肺炎患者的死亡率 显示英文标题

标题： Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression

摘要： 在持续的新冠疫情中，高风险新冠患者是否能够康复在很大程度上取决于他们在病毒造成不可逆后果之前能否得到适当的治疗。 在这项研究中，我们报告了一个基于逻辑回归的可解释、直观且准确的机器学习模型，该模型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C反应蛋白及其相互作用，来预测新冠患者的死亡率。 我们发现，当逻辑回归模型产生的死亡概率超过0.8时，该模型表现出最佳性能，能够在平均11.30天内，最多提前34.91天预测患者死亡，并且累积F1分数为93.76%，累积准确度得分为93.92%。 这样的模型可以用于通过三种血液生物标志物识别高风险新冠患者，并帮助全球医疗系统在这个疫情期间规划关键的医疗资源。 显示英文摘要

摘要： 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.