Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction.
Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction.
复制标题
基于机器学习的临床决策支持系统，用于早期Covid-19死亡率预测。
DOI：
10.3389/fpubh.2021.626697
复制
发表时间：
2021
影响因子：
5.2
通讯作者：
Priyakumar UD
中科院分区：
文献类型：
--
作者：
Karthikeyan A;Garg A;Vinod PK;Priyakumar UD
由SARS-CoV-2病毒引起的冠状病毒病2019 （COVID-19）是一种被世界卫生组织（WHO）列为大流行的急性呼吸道疾病。感染人数的突然飙升和高死亡率给公共卫生系统带来了巨大的压力。因此，确定死亡率预测的关键因素以优化患者的治疗策略至关重要。与预测死亡率的x光、ct扫描和超声波等其他形式的数据相比，常规血液检查结果广泛可用。本研究提出了基于血液检测数据的机器学习（ML）方法来预测COVID-19的死亡风险。中性粒细胞、淋巴细胞、乳酸脱氢酶（LDH）、高敏c反应蛋白（hs-CRP）和年龄这五个特征的强大组合有助于预测死亡率，准确率为96%。各种机器学习模型（神经网络、逻辑回归、XGBoost、随机森林、支持向量机和决策树）已经进行了训练和性能比较，以确定在跨越疾病的天数内始终保持高精度的模型。使用XGBoost特征重要性和神经网络分类的最佳方法，可以在结果出现前16天预测准确率达到90%。基于天结果的三个案例的鲁棒性测试证实了所提出模型的强大预测性能和实用性。使用这些关键生物标志物进行了详细的分析和趋势识别，为直观的应用提供了有用的见解。本研究提供的解决方案将有助于加快医疗保健系统的决策过程，以准确、早期和可靠的方式进行重点医疗。
The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.
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影响因子：
7.4
作者：
Ikemura K;Bellin E;Yagi Y;Billett H;Saada M;Simone K;Stahl L;Szymanski J;Goldstein DY;Reyes Gil M
通讯作者：
Reyes Gil M
影响因子：
3.9
作者：
Callejon-Leblic MA;Moreno-Luna R;Del Cuvillo A;Reyes-Tejero IM;Garcia-Villaran MA;Santos-Peña M;Maza-Solano JM;Martín-Jimenez DI;Palacios-Garcia JM;Fernandez-Velez C;Gonzalez-Garcia J;Sanchez-Calvo JM;Solanellas-Soler J;Sanchez-Gomez S
通讯作者：
Sanchez-Gomez S
影响因子：
4.8
作者：
Elith, J.;Leathwick, J. R.;Hastie, T.
通讯作者：
Hastie, T.
影响因子：
3.7
作者：
Guan WZ;Qiu GF;Feng-Liu
通讯作者：
Feng-Liu
影响因子：
3.6
作者：
Henry, Brandon Michael;Aggarwal, Gaurav;Lippi, Giuseppe
通讯作者：
Lippi, Giuseppe