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首页> 外文期刊>JMIR Medical Informatics >Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development
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Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

机译：基于临床血液测试数据预测Covid-19疾病严重程度的机器学习方法：统计分析和模型开发

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Background Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores 90% for disease severity prediction. Conclusions We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
机译：背景技术精确预测Covid-19患者的疾病严重程度将大大提高护理递送和资源分配，从而降低死亡率风险，特别是在较不发达国家。许多与患者相关的因素，例如预先存在的合并症，影响疾病严重程度，并且可用于帮助这种预测。目的，因为外周血样品的快速自动分析广泛可用，我们旨在调查Covid-19患者外周血的数据如何用于预测临床结果。方法采用机器学习算法组合统计比较和相关方法，研究了Covid-19患者的临床数据集。后者包括决策树，随机森林，梯度升压机的变体，支持向量机，K最近邻居和深度学习方法。结果我们的工作表明，血液样本中可测量的几个临床参数是可以区分健康人和Covid-19阳性患者的因素，并且我们展示了这些参数的价值预测Covid-19症状的后期严重程度。我们开发了许多分析方法，显示出准确性和精确分数＆ 90％用于疾病严重程度预测。结论我们开发了分析常规患者临床数据的方法，使能够更准确地预测Covid-19患者结果。通过这种方法，来自患者血液的标准医院实验室分析的数据可用于鉴定患有高死亡风险的Covid-19患者，从而能够优化用于Covid-19治疗的医院设施。

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