Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.
Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.
复制标题
DOI：
10.1109/access.2020.3034032
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Valentini G
中科院分区：
文献类型：
--
作者：
Casiraghi E;Malchiodi D;Trucco G;Frasca M;Cappelletti L;Fontana T;Esposito AA;Avola E;Jachetti A;Reese J;Rizzi A;Robinson PN;Valentini G
在2020年1月至10月期间，严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒在全球范围内感染了超过3400万人，导致全球超过100万人死亡（来自约翰霍普金斯大学的数据）。自从病毒开始传播以来，急诊科就一直在忙碌于新冠肺炎患者，他们需要迅速决定是否接受住院或门诊治疗。该病毒可导致胸部X光片（CXR）的特征性异常，但由于CXR的敏感性低，需要额外的变量和标准来准确预测风险。在这里，我们描述了一个计算机化的系统，其主要目的是提取最相关的放射学，临床和实验室变量，以改善患者风险预测，其次是提出一个可解释的机器学习系统，该系统可以提供简单的决策标准，供临床医生使用，作为评估患者风险的支持。为了实现稳健和可靠的变量选择，Boruta和随机森林（RF）结合在一个10倍交叉验证方案，以产生一个变量的重要性估计不存在替代偏倚。然后选择最重要的变量来训练RF分类器，其规则可以被提取、简化和修剪以最终构建关联树，特别是其简单性。结果表明，通过神经网络自动计算的放射学评分与放射科医生计算的评分高度相关，实验室变量以及合并症的数量有助于风险预测。我们的方法的预测性能进行了比较，广义线性模型，并被证明是有效的和强大的。所提出的基于机器学习的计算系统可以很容易地部署和用于急诊科，以快速准确地预测COVID-19患者的风险。
Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.