Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19

The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective detections like Reverse Transcription Polymerase...

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Bibliographic Details
Main Authors:	Guo, G. (Author), Guo, L. (Author), Liu, T. (Author), Liu, Z. (Author), Zhao, S. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	algorithm Algorithms Area Under Curve area under the curve Article artificial neural network C reactive protein Classification (of information) Classification algorithm Classification methods Classification performance Clinical diagnosis clinical spectrum Complementary methods Computer aided diagnosis computer assisted tomography Computerized tomography coronavirus disease 2019 COVID-19 COVID-19 diagnosis COVID-19 Testing Databases, Factual Decision trees Developing countries diagnosis diagnostic accuracy diagnostic imaging factual database Functional polymers gradient boosting hemoglobin human Humans indefiniteness elimination indefiniteness elimination network mathematical model measurement precision Multi layer perceptron Multilayer neural networks multilayer perceptron myeloblast neural network Neural Networks, Computer Polymerase chain reaction quick screening random forest reproducibility Reproducibility of Results reverse transcription polymerase chain reaction Reverse transcription-polymerase chain reaction screening Screening methods time factor Time Factors virus detection
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Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19

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