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10.7507/1001-5515.202003045 http://scihub22266oqcxt.onion/10.7507/1001-5515.202003045 32597076!10319565!32597076     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32597076&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538       Sheng+Wu+Yi+Xue+Gong+Cheng+Xue+Za+Zhi 2020 ; 37 (3): 365-372 Nephropedia Template TP {{tp|p=32597076|t=2020. Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19 |pdf=|usr=}}{{32597076}} gab.com Text Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19 JO: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32597076 #FOAvip The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently. Twit Text FOAVip Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19 ????Sheng Wu Yi Xue Gong Cheng Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=32597076 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32597076 #FOAvip English Wikipedia <ref>{{Cite pmid|32597076}}</ref> Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19 #MMPMID32597076 Pu X; Chen K; Liu J; Wen J; Zhneng S; Li H Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020[Jun]; 37 (3): 365-372 PMID32597076show ga The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently. |*Machine Learning[MESH] |*Practice Guidelines as Topic[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Coronavirus Infections/*diagnosis/*therapy[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/*therapy[MESH] Machine learning-based method for interpreting the guidelines of the (epmc).pdf DeepDyve Pubget Overpricing