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杨文静, 杜然然, 吕章艳, 冯小双, 任建松, 池慧. 人工智能在疾病预测研究中可视化分析[J]. 中国公共卫生, 2021, 37(5): 871-874. DOI: 10.11847/zgggws1128486
引用本文: 杨文静, 杜然然, 吕章艳, 冯小双, 任建松, 池慧. 人工智能在疾病预测研究中可视化分析[J]. 中国公共卫生, 2021, 37(5): 871-874. DOI: 10.11847/zgggws1128486
YANG Wen-jing, DU Ran-ran, LÜ Zhang-yan, . Application of artificial intelligence in disease prediction researches: a visual analysis[J]. Chinese Journal of Public Health, 2021, 37(5): 871-874. DOI: 10.11847/zgggws1128486
Citation: YANG Wen-jing, DU Ran-ran, LÜ Zhang-yan, . Application of artificial intelligence in disease prediction researches: a visual analysis[J]. Chinese Journal of Public Health, 2021, 37(5): 871-874. DOI: 10.11847/zgggws1128486

人工智能在疾病预测研究中可视化分析

Application of artificial intelligence in disease prediction researches: a visual analysis

  • 摘要:
      目的  以可视化方式展示国际人工智能在疾病预测应用领域的研究现状及热点前沿,以期为中国人工智能在疾病预测中的应用研究提供参考依据。
      方法  应用文献计量学方法检索Web of Science数据库2010 — 2019年国内外人工智能在疾病预测中应用研究的文献,通过Cite Space软件分别从国家/机构合作、知识基础、研究热点、研究前沿等方面进行可视化分析。
      结果  共检索文献4323篇,发文量居于前3位的国家依次为美国(1484篇)、中国(1032篇)和英国(300篇)。国家合作能力居于前3位的国家依次为法国、美国、英国,中心度值分别为0.18、0.16、0.16;中国居于第8位,中心度值为0.08。研究的疾病领域主要集中在癌症,研究的方法主要有机器学习、人工神经网络等,研究关注的热点主要是疾病的诊断和预后等,研究前沿除了方法学突破还有药学研究等。
      结论  中国十分重视人工智能在疾病预测领域的研究,但研究质量和影响力有待提高,需进一步关注研究热点和前沿,加强国际合作,促进我国人工智能在疾病预测领域的发展。

     

    Abstract:
      Objective  To examine status and hotspots of artificial intelligence (AI) application in researches on disease prediction at home and abroad with visual analysis for providing references to researches on AI application in disease prediction in China.
      Methods  Bibliometrics method was used to retrieve domestic and foreign literatures of researches on AI application in disease prediction published from 2010 through 2019 via searching Web of Science database. Visual analysis was performed using Cite Space software to describe country/institution cooperation, knowledge base, research hotspots and frontiers of disease prediction researches involving AI application.
      Results  Totally 4 323 literatures were retrieved. The top three countries in terms of number of published literatures are the United States (1 484), China (1 032) and the United Kingdom (300); the top three countries in terms of national cooperation capability are France, the United States, and the United Kingdom, with the centrality values of 0.18, 0.16, and 0.16, respectively; China ranks the eighth in terms of national cooperation capability, with a centrality value of 0.08. Among the retrieved AI application studies, the most studied disease was cancer; the most adopted methods were machine learning and artificial neural network; and the most concerned research topics were disease diagnosis and prognosis.
      Conclusion  Scholars in China have performed a great deal of studies on artificial intelligence application in researches on disease prediction but the quality and influence of the studies need to be improved.

     

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