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arXiv 2006.03476physics.med-phcs.LGq-bio.QMstat.ML

使用机器学习通过常规血液检测诊断COVID-19

COVID-19 diagnosis by routine blood tests using machine learning

Smart Blood Analytics Swiss SA（智能血液分析瑞士公司）；Faculty of Computer and Information Science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）；Faculty of Chemistry and Chemical Technology, University of Ljubljana（卢布尔雅那大学化学与化学技术学院）；University Medical Centre Ljubljana（卢布尔雅那大学医学中心）

Matjaž Kukar, Gregor Gunčar, Tomaž Vovko, Simon Podnar, Peter Černelč, Miran Brvar, Mateja Zalaznik, Mateja Notar, Sašo Moškon, Marko Notar

备注：
11 pages, 4 figures, 2 tables
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AI总结：

本研究利用机器学习模型基于常规血液检测诊断COVID-19，模型在5333名感染患者和160名COVID-19患者上交叉验证，AUC达0.97，灵敏度81.9%，特异度97.9%，识别出五个关键血液参数，为COVID-19诊断提供快速辅助工具。

摘要：

照顾冠状病毒病（COVID-19）患者的医生描述了常规血液参数的不同变化。然而，这些变化阻碍了他们进行COVID-19诊断。我们构建了一个用于COVID-19诊断的机器学习预测模型。该模型基于5,333名患有各种细菌和病毒感染的患者以及160名COVID-19阳性患者的常规血液检测结果，并进行了交叉验证。我们选择了灵敏度为81.9%、特异度为97.9%的操作性ROC点。交叉验证的曲线下面积（AUC）为0.97。根据XGBoost算法的特征重要性评分，对COVID-19诊断最有用的五个常规血液参数是MCHC、嗜酸性粒细胞计数、白蛋白、INR和凝血酶原活性百分比。tSNE可视化显示，重症COVID-19病程患者的血液参数更类似于细菌感染而非病毒感染的参数。所报告的诊断准确性与RT-PCR和胸部CT研究相比至少相当，并且可能互补。现在，有发热、咳嗽、肌痛和其他症状的患者可以通过我们的诊断工具进行初步常规血液检测评估。所有COVID-19预测为阳性的患者随后将接受标准RT-PCR研究以确认诊断。我们相信我们的结果对改善COVID-19诊断做出了重要贡献。

英文摘要：

Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.

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