COVID-19 中国诊疗方案的机器学习智能分析方法

蒲晓蓉; 陈柯成; 刘军池; 文进; 郑尚维; 李鸿浩

doi:10.7507/1001-5515.202003045

COVID-19 中国诊疗方案的机器学习智能分析方法

doi: 10.7507/1001-5515.202003045

蒲晓蓉^{1, 2},
陈柯成^{1, 2},
刘军池^{1, 2},
文进³,
郑尚维³,
李鸿浩^3, ,

详细信息

通讯作者:
李鸿浩，Email：243424375@qq.com

计量
- 文章访问数: 320
- HTML全文浏览量: 182
- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2020-03-16
- 修回日期: 2020-03-25
- 发布日期: 2020-03-17

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

Xiaorong PU^{1, 2},
Kecheng CHEN^{1, 2},
Junchi LIU^{1, 2},
Jin WEN³,
Shangwei ZHNENG³,
Honghao LI^{3
, ,}

More Information

Corresponding author: LI Honghao, Email: 243424375@qq.com

摘要

摘要: 2019 年底暴发的新型冠状病毒肺炎（COVID-19）疫情是人类史上一次重大突发公共卫生事件。中国医学工作者在短时间内，经历了对该未知病毒的逐步认识、证据积累和临床实践。截至目前，中国国家卫生健康委员会在数十天内密集发布了七个版本的《新型冠状病毒感染的肺炎诊疗方案》（简称《诊疗方案》）。然而，快速准确地比较各版本的异同和掌握新版本的重点对临床医护人员和非专业人员来说存在一定困难。本文提出一种基于机器学习的计算机辅助智能分析方法，对文本主题进行无监督学习，自动分析不同版本《诊疗方案》的异同，主动给医护人员推送新版本的关注重点，降低《诊疗方案》解读的专业难度，提高非专业人员对诊疗方案的认识水平。实验证明，与人工解读方式相比较，本文方法能自动计算文本主题，实现主题的精准匹配，准确率达 100%，并可自动生成关键词和语句级别的解读报告，实现《诊疗方案》的计算机自动智能解读。
- COVID-19 /
- 新型冠状病毒 /
- 人工智能 /
- 机器学习 /
- 诊疗方案 /
- 生物医学工程
Abstract: 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.
- COVID-19 /
- novel coronavirus /
- artificial intelligence /
- machine learning /
- diagnosis and treatment plan /
- biomedical engineering

HTML全文

参考文献(14)

[1]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第一版)[EB/OL]. (2020-01-16) [2020-03-24]. http://www.nhc.gov.cn/yzygj/s7659/202001/b91fdab7c304431eb082d67847d27e14.shtml.
[2]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第二版)[EB/OL]. (2020-01-18) [2020-03-24]. http://www.360doc.com/content/20/0202/00/397911_889144879.shtml.
[3]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第三版)[EB/OL]. (2020-01-23) [2020-03-24]. http://www.nhc.gov.cn/xcs/yqfkdt/202001/f492c9153ea9437bb587ce2ffcbee1fa.shtml.
[4]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第四版)[EB/OL]. (2020-01-27) [2020-03-24]. http://www.nhc.gov.cn/yzygj/s7653p/202001/4294563ed35b43209b31739bd0785e67.shtml.
[5]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第五版)[EB/OL]. (2020-02-04) [2020-03-24]. http://www.nhc.gov.cn/xcs/zhengcwj/202002/3b09b894ac9b4204a79db5b8912d4440.shtml.
[6]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第六版)[EB/OL]. (2020-02-19) [2020-03-24]. http://www.nhc.gov.cn/yzygj/s7653p/202002/8334a8326dd94d329df351d7da8aefc2.shtml.
[7]	国家卫生健康委员会, 国家中医药管理局. 新型冠状病毒感染的肺炎诊疗方案(试行第七版)[EB/OL]. (2020-03-04) [2020-03-24]. http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml.
[8]	Anderson J A. Cognitive and psychological computation with neural models. IEEE Trans Syst Man Cybern, 1983, SMC-13(5): 799-815.
[9]	Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. J Mach Learn Res, 2003, 2003(5): 993-1022.
[10]	Casella G, George E I. Explaining the Gibbs sampler. The American Statistician, 1992, 46(3): 167-174.
[11]	Gaussier E, Goutte C. Relation between PLSA and NMF and implications// Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2005: 601-602.
[12]	Lu Y, Mei Q, Zhai C. Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA. Information Retrieval, 2011, 14(2): 178-203.
[13]	Ramos J. Using TF-IDF to determine word relevance in document queries// Proceedings of the First Instructional Conference on Machine Learning. Piscataway: 2003: 33-142.
[14]	Pu X, Long K, Chen K, et al. A semantic-based short-text fast clustering method on hotline records in Chengdu// 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). Fukuoka: IEEE, 2019: 516-521.