首页> 美国卫生研究院文献>Journal of Multidisciplinary Healthcare >Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs Laboratory and Clinical Data with Machine Learning

【2h】

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs Laboratory and Clinical Data with Machine Learning

机译：从机器学习的常规收集基线胸部射线照片的Covid-19通风要求和死亡率的早期预测

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

摘要
著录项
相似文献
相关主题

摘要

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type.

机译：由严重急性呼吸综合征冠状病毒2（SARS-COV-2）引起的冠状病毒疾病2019（Covid-19），在中国武汉出现在2019年底，并创建了一个全球大流行，不堪重负医疗保健系统。截至2021年7月3日的Covid-19，根据世界卫生组织根据世界卫生组织在全球范围内得到18200万条确诊的案件和390万人死亡。几种患者最初被诊断为轻度或中度Covid-19后来恶化，并重新分类为严重的疾病类型。

著录项

期刊名称 Journal of Multidisciplinary Healthcare
作者
Abdulrhman Fahad Aljouie; Ahmed Almazroa; Yahya Bokhari; Mohammed Alawad; Ebrahim Mahmoud; Eman Alawad; Ali Alsehawi; Mamoon Rashid; Lamya Alomair; Shahad Almozaai; Bedoor Albesher; Hassan Alomaish; Rayyan Daghistani; Naif Khalaf Alharbi; Manal Alaamery; Mohammad Bosaeed; Hesham Alshaalan;
展开▼
作者单位

展开▼
年(卷),期 2021(-1),-1
年度 2021
页码 -1
总页数 17
原文格式 PDF
正文语种
中图分类公共卫生工程;
关键词

机译：Covid-19;死亡率;核心;X射线;CBC;随机森林;笑容;机器学习;

相似文献

外文文献
专利

1. Neonatal mortality prediction with routinely collected data: a machine learning approach [J] . Batista André F. M., Diniz Carmen S. G., Bonilha Eliana A., BMC Pediatrics . 2021,第1期

机译：新生儿死亡率预测常规收集的数据：机器学习方法
2. Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach [J] . Daowei Li, Qiang Zhang, Yue Tan, JMIR Medical Informatics . 2020,第11期

机译：利用胸部计算断层扫描和实验室测量预测Covid-19严重程度：使用机器学习方法进行评估
3. Typical and atypical CT chest imaging findings of novel coronavirus 19 (COVID-19) in correlation with clinical data: impact on the need to ICU admission, ventilation and mortality [J] . Emara Doaa M., Naguib Nagy N., Moustafa M. A., The Egyptian Journal of Radiology and Nuclear Medicine . 2020,第1期

机译：新型冠状病毒19（Covid-19）的典型和非典型CT胸部成像结果与临床数据相关性：对ICU入院，通风和死亡的需求的影响
4. Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning [C] . Paula de Toledo, Rodrigo Pérez-Rodríguez, Pablo de Miguel, Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2019

机译：根据临床风险组预测患者的病情演变，这些数据是使用机器学习定期收集的数据
5. Using Routine Laboratory Results and Machine Learning to Predict the 45-Day Mortality of Gynecological Oncology and Breast Oncology Inpatients [D] . Colorado, Cesar. 2020

机译：使用常规实验室结果和机器学习预测妇科肿瘤学和乳腺肿瘤学院的45天死亡率
6. Neonatal mortality prediction with routinely collected data: a machine learning approach [O] . André F. M. Batista, Carmen S. G. Diniz, Eliana A. Bonilha, 2021

机译：常规收集数据的新生儿死亡率预测：机器学习方法
7. Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation [O] . Juan L Domínguez-Olmedo, Álvaro Gragera-Martínez, Jacinto Mata, 2021

机译：机器学习应用于西班牙Covid-19结果预测的临床实验室数据：模型开发与验证

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs Laboratory and Clinical Data with Machine Learning

摘要

著录项

相似文献

相关主题

期刊订阅