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Xiaorong PU, Kecheng CHEN, Junchi LIU, Jin WEN, Shangwei ZHNENG, Honghao LI. Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 365-372. doi: 10.7507/1001-5515.202003045
Citation: Xiaorong PU, Kecheng CHEN, Junchi LIU, Jin WEN, Shangwei ZHNENG, Honghao LI. Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 365-372. doi: 10.7507/1001-5515.202003045

Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19

doi: 10.7507/1001-5515.202003045
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  • Corresponding author: LI Honghao, Email: 243424375@qq.com
  • Received Date: 16 Mar 2020
  • Rev Recd Date: 25 Mar 2020
  • Publish Date: 17 Mar 2020
  • 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.

     

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