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10.1109/JBHI.2021.3060035 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3060035 33600327!8545172!33600327     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2021 ; 25 (5): 1347-1357 Nephropedia Template TP {{tp|p=33600327|t=2021. Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19 |pdf=|usr=}}{{33600327}} gab.com Text Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19 JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33600327 #FOAvip 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 Chain Reaction (RT-PCR) tests or CT scans. This motivates us to develop a quick screening method via common clinical diagnosis results. However, the diagnostic items of different patients may vary greatly, and there is a huge variation in the dimension of the diagnosis data among different suspected patients, it is hard to process these indefinite dimension data via classical classification algorithms. To resolve this problem, we propose an Indefiniteness Elimination Network (IE-Net) to eliminate the influence of the varied dimensions and make predictions about the COVID-19 cases. The IE-Net is in an encoder-decoder framework fashion, and an indefiniteness elimination operation is proposed to transfer the indefinite dimension feature into a fixed dimension feature. Comprehensive experiments were conducted on the public available COVID-19 Clinical Spectrum dataset. Experimental results show that the proposed indefiniteness elimination operation greatly improves the classification performance, the IE-Net achieves 94.80% accuracy, 92.79% recall, 92.97% precision and 94.93% AUC for distinguishing COVID-19 cases from non-COVID-19 cases with only common clinical diagnose data. We further compared our methods with 3 classical classification algorithms: random forest, gradient boosting and multi-layer perceptron (MLP). To explore each clinical test item's specificity, we further analyzed the possible relationship between each clinical test item and COVID-19. Twit Text FOAVip Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19 ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33600327 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33600327 #FOAvip English Wikipedia <ref>{{Cite pmid|33600327}}</ref> Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19 #MMPMID33600327 Guo G; Liu Z; Zhao S; Guo L; Liu T IEEE J Biomed Health Inform 2021[May]; 25 (5): 1347-1357 PMID33600327show ga 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 Chain Reaction (RT-PCR) tests or CT scans. This motivates us to develop a quick screening method via common clinical diagnosis results. However, the diagnostic items of different patients may vary greatly, and there is a huge variation in the dimension of the diagnosis data among different suspected patients, it is hard to process these indefinite dimension data via classical classification algorithms. To resolve this problem, we propose an Indefiniteness Elimination Network (IE-Net) to eliminate the influence of the varied dimensions and make predictions about the COVID-19 cases. The IE-Net is in an encoder-decoder framework fashion, and an indefiniteness elimination operation is proposed to transfer the indefinite dimension feature into a fixed dimension feature. Comprehensive experiments were conducted on the public available COVID-19 Clinical Spectrum dataset. Experimental results show that the proposed indefiniteness elimination operation greatly improves the classification performance, the IE-Net achieves 94.80% accuracy, 92.79% recall, 92.97% precision and 94.93% AUC for distinguishing COVID-19 cases from non-COVID-19 cases with only common clinical diagnose data. We further compared our methods with 3 classical classification algorithms: random forest, gradient boosting and multi-layer perceptron (MLP). To explore each clinical test item's specificity, we further analyzed the possible relationship between each clinical test item and COVID-19. |*COVID-19 Testing[MESH] |*Neural Networks, Computer[MESH] |Algorithms[MESH] |Area Under Curve[MESH] |COVID-19/*diagnosis[MESH] |Databases, Factual[MESH] |Humans[MESH] |Reproducibility of Results[MESH]Eliminating Indefiniteness of Clinical Spectrum for Better Screening C(epmc).pdf DeepDyve Pubget Overpricing