Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.
Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.
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用机器学习预测ICU中新冠肺炎患者第0天的死亡率。
DOI：
10.3389/fdgth.2021.681608
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发表时间：
2021
影响因子：
--
通讯作者：
Mansouri N
中科院分区：
文献类型：
--
作者：
Jamshidi E;Asgary A;Tavakoli N;Zali A;Setareh S;Esmaily H;Jamaldini SH;Daaee A;Babajani A;Sendani Kashi MA;Jamshidi M;Jamal Rahi S;Mansouri N
理由：鉴于 COVID-19 病例数量不断增加以及新一波感染的可能性，迫切需要早期预测重症监护病房 (ICU) 患者疾病的严重程度，以优化治疗策略。目标：根据典型的实验室结果和入住 ICU 当天登记的临床数据，使用机器学习来早期预测死亡率。方法：我们回顾性研究了伊朗和英国 (U.K) 797 名被诊断患有 COVID-19 的患者。为了找到具有最高预测值的参数，使用了 Kolmogorov-Smirnov 和 Pearson 卡方检验。利用多种机器学习算法，包括随机森林（RF）、逻辑回归、梯度提升分类器、支持向量机分类器和人工神经网络算法来构建分类模型。通过实施局部可解释模型不可知解释技术 (LIME-SP)，研究了每个标记对 RF 模型预测的影响。结果：在 66 个记录参数中，确定了 15 个具有最高预测值的因素：性别、年龄、血尿素氮 (BUN)、肌酐、国际标准化比率 (INR)、白蛋白、平均红细胞体积 (MCV)、白细胞计数、分段中性粒细胞计数、淋巴细胞计数、红细胞分布宽度 (RDW) 和平均细胞血红蛋白 (MCH) 以及神经、心血管和呼吸系统疾病史。我们的 RF 模型可以预测患者结果，灵敏度为 70%，特异性为 75%。通过在外部数据集中盲目测试模型来确认模型的性能。结论：使用两个独立的患者数据集，我们设计了一个基于机器学习的模型，可以高精度预测重症 COVID-19 的死亡风险。我们模型中最具决定性的变量是 BUN 水平升高、白蛋白水平降低、肌酐、INR 和 RDW 升高，以及性别和年龄。考虑到早期分诊决策的重要性，该模型可以成为 COVID-19 ICU 决策的有用工具。
Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.
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影响因子：
1.5
作者：
Agarwal, Shikhar
通讯作者：
Agarwal, Shikhar
机器学习方法以识别患者合并症和症状增加COVID-19的死亡风险。
DOI：
10.3390/diagnostics11081383
发表时间：
2021-07-31
期刊：
Diagnostics (Basel, Switzerland)
影响因子：
--
作者：
Aktar S;Talukder A;Ahamad MM;Kamal AHM;Khan JR;Protikuzzaman M;Hossain N;Azad AKM;Quinn JMW;Summers MA;Liaw T;Eapen V;Moni MA
通讯作者：
Moni MA
DOI：
10.1016/j.scitotenv.2020.142810
发表时间：
2021-04-10
期刊：
The Science of the total environment
影响因子：
--
作者：
Li M;Zhang Z;Cao W;Liu Y;Du B;Chen C;Liu Q;Uddin MN;Jiang S;Chen C;Zhang Y;Wang X
通讯作者：
Wang X
影响因子：
4.5
作者：
de la Rica, Roberto;Borges, Marcio;Gonzalez-Freire, Marta
通讯作者：
Gonzalez-Freire, Marta
影响因子：
1.8
作者：
Horton, NJ;Lipsitz, SR;Parzen, M
通讯作者：
Parzen, M