机器学习模型的黑箱特性阻碍了一些高精度医疗诊断算法的部署。把一个人的生命交到医学研究人员不完全理解或信任的模型手中是有风险的。然而,通过模型解释,黑盒模型可以及时揭示重要的生物标志物,这些生物标志物可能是医疗人员由于COVID-19大流行中感染患者的激增而忽略的。这项研究利用了在2020年1月18日至2020年3月5日期间在珠海进行的92名确诊SARS-CoV-2实验室检查的患者的数据库。中国,确定指示感染严重程度预测的生物标志物。通过对四种机器学习模型的解读,决策树,随机森林,梯度增强树,和使用置换特征重要性的神经网络,部分依赖图,个人条件期望,累积的局部效应,本地可解释的模型-不可知的解释,和Shapley加法解释,我们发现N末端脑钠肽前体增加,C反应蛋白,和乳酸脱氢酶,淋巴细胞减少与严重感染和死亡风险增加有关,这与最近关于COVID-19的医学研究和其他使用专用模型的研究是一致的。我们在一个大型开放数据集上进一步验证了我们的方法,其中5644名来自以色列医院阿尔伯特·爱因斯坦的确诊患者,在圣保罗,来自Kaggle的巴西,揭开白细胞的面纱,嗜酸性粒细胞,和血小板作为COVID-19的三种指示性生物标志物。
The black-box nature of machine learning models hinders the deployment of some high-accuracy medical diagnosis algorithms. It is risky to put one\'s life in the hands of models that medical researchers do not fully understand or trust. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai,
China, to identify biomarkers indicative of infection severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, partial dependence plot, individual conditional expectation, accumulated local effects, local interpretable model-agnostic explanations, and Shapley additive explanation, we identify an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, a decrease in lymphocyte is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.