Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making.
Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making.
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DOI：
10.1016/j.smhl.2020.100178
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Shakibi M
中科院分区：
文献类型：
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作者：
Pourhomayoun M;Shakibi M
在 SARS-CoV-2 病毒引起的 COVID-19 疾病爆发后，我们设计并开发了一种基于人工智能 (AI) 和机器学习算法的预测模型，以确定 COVID-19 患者的健康风险并预测死亡风险。在这项研究中，我们使用了来自全球 146 个国家的超过 2,670,000 名实验室确诊的 COVID-19 患者的数据集，其中包括 307,382 个标记样本。这项研究提出了一种人工智能模型，可以帮助医院和医疗机构决定谁需要首先得到关注，谁有更高的住院优先级，在系统因过度拥挤而不堪重负时对患者进行分类，并消除提供必要护理方面的延误。结果表明，预测死亡率的总体准确度为 89.98%。我们使用了多种机器学习算法，包括支持向量机 (SVM)、人工神经网络、随机森林、决策树、逻辑回归和 K 最近邻 (KNN) 来预测 COVID-19 患者的死亡率。在这项研究中，还确定了最令人担忧的症状和特征。最后，我们使用单独的 COVID-19 患者数据集来评估我们开发的模型的准确性，并使用混淆矩阵对我们的分类器进行深入分析并计算我们模型的敏感性和特异性。
In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 89.98% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.