About: Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results

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About: Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results
Creator	Li, Junfeng Cai, Jing Xie, Huang, Huirong Li, Shuyan Li, Yonghong Meng, Wenbo Tong, Chongxiang Wu, Jiangpeng Xie, Xiaodong Yang, Zengwei Zhang, Liting Zhang, Pengyi Zhao, Meie Zhu, Jinhong
Source	MedRxiv
abstract	Since the sudden outbreak of coronavirus disease 2019 (COVID-19), it has rapidly evolved into a momentous global health concern. Due to the lack of constructive information on the pathogenesis of COVID-19 and specific treatment, it highlights the importance of early diagnosis and timely treatment. In this study, 11 key blood indices were extracted through random forest algorithm to build the final assistant discrimination tool from 49 clinical available blood test data which were derived by commercial blood test equipments. The method presented robust outcome to accurately identify COVID-19 from a variety of suspected patients with similar CT information or similar symptoms, with accuracy of 0.9795 and 0.9697 for the cross-validation set and test set, respectively. The tool also demonstrated its outstanding performance on an external validation set that was completely independent of the modeling process, with sensitivity, specificity, and overall accuracy of 0.9512, 0.9697, and 0.9595, respectively. Besides, 24 samples from overseas infected patients with COVID-19 were used to make an in-depth clinical assessment with accuracy of 0.9167. After multiple verification, the reliability and repeatability of the tool has been fully evaluated, and it has the potential to develop into an emerging technology to identify COVID-19 and lower the burden of global public health. The proposed tool is well-suited to carry out preliminary assessment of suspected patients and help them to get timely treatment and quarantine suggestion. The assistant tool is now available online at http://lishuyan.lzu.edu.cn/COVID2019_2/.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1101/2020.04.02.20051136
has license	medrxiv
sha1sum (hex)	8c0b398abd2ce9151a30e14e324a2d91b6b850ce
schema:url	https://doi.org/10.1101/2020.04.02.20051136
resource representing a document's title	Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results
resource representing a document's body	covid:8c0b398abd2ce9151a30e14e324a2d91b6b850ce#body_text
is schema:about of	named entity 'IDENTIFICATION' named entity 'global health' named entity 'BASED' named entity 'RAPID' named entity 'GLOBAL HEALTH' named entity 'EARLY DIAGNOSIS' named entity 'CONCERN' named entity 'CORONAVIRUS DISEASE 2019' named entity 'MACHINE LEARNING' named entity 'EVOLVED' named entity 'DUE TO' named entity 'TIMELY' named entity 'BLOOD TEST RESULTS' named entity 'AVAILABLE' named entity 'INFORMATION' named entity 'CLINICAL' named entity 'TREATMENT' named entity 'PATHOGENESIS' named entity 'OUTBREAK' named entity 'SUDDEN' named entity 'SPECIFIC' named entity 'LACK' named entity 'RAPIDLY' named entity 'COVID-19' named entity 'ACCURATE' covid:arg/8c0b398abd2ce9151a30e14e324a2d91b6b850ce named entity 'pathogenesis' named entity 'infection' named entity 'COVID-19' named entity 'MCC' named entity 'irreplaceable' named entity 'peer-reviewed' named entity 'preprint' named entity 'COVID-19' named entity 'hematological' named entity 'medRxiv' named entity 'COVID' named entity 'bioinformatics' named entity 'creatinine' named entity 'COVID' named entity 'magnesium' named entity 'Gansu Province' named entity 'Matthews correlation coefficient' named entity 'medRxiv' named entity 'infection' named entity 'global public health' named entity 'qRT-PCR' named entity 'web server' named entity 'coronavirus' named entity 'preprint' named entity 'COVID' named entity 'severe epidemic' named entity 'clinical characteristics' named entity 'platelet' named entity 'training set' named entity 'cough' named entity 'COVID' named entity 'randomly selected' named entity 'IgG' named entity 'COVID-19' named entity 'validation set' named entity 'COVID-19' named entity 'COVID' named entity 'preprint' named entity 'infection' named entity 'immunoglobulin' named entity 'infection' named entity 'CK-MB' named entity 'text box' named entity 'respiratory illness' named entity 'chemometrics'

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