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人工智能技术在药学领域的应用——基于Web of Science的文献可视化分析
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| 篇名： | 人工智能技术在药学领域的应用——基于Web of Science的文献可视化分析 |
| TITLE： | |
| 摘要： | 目的：了解人工智能（AI）技术应用于药学领域的研究概况、热点及前沿进展，为我国相关研究的发展提供思路。方法：采用文献计量学方法，在Web of Science数据库中检索1998－2017年发表的相关期刊和会议论文（检索词为“Article”和“Proceeding Paper”）；利用ISI Web of Knowledge自带的分析检索结果及创建引文报告功能，结合CiteSpace 5.2.R1软件绘制知识图谱，对目标文献进行定量统计和定性分析，对该领域研究的发文量、共被引情况、主要研究国家/地区、主要研究机构、主要研究者、研究热点及研究前沿进行归纳总结。结果：共检索得3 674篇相关文献。1998－2017年期间该领域文献数量飞速增长；美国、中国、英国和德国因发文量大而占据领先地位，但中国的国际合作明显较少，且缺乏优秀核心团队；从研究者角度看，该领域研究处于“部分集中、整体分散”的状态，缺乏团队合作。该领域的研究热点包括AI技术的重要核心（机器学习算法）以及其在药学领域的主要运用（药物发现及设计），还有疾病或不良反应诊断分级、药学模型的建立和优化、药物筛选或药效预测、药学数据库的建立等；近年来的研究前沿包括“分子对接”“机器学习”“Meta分析”“精准用药” “靶向治疗”等。结论：AI技术在药学领域的应用是一个时效性极强的热门研究领域，其应用于医药产业开发是大势所趋，而我国在该领域的研究现状与国际前沿水平仍存在一定差距。这需要我国药学工作者在做好实验研究和临床试验等基础工作的同时，加强与AI领域专家的的交流和合作，以适应AI技术与药学紧密结合发展的国际趋势。 |
| ABSTRACT： | OBJECTIVE： To investigate the research status，hotspots and frontiers of the artificial intelligence （AI） technology applicated in pharmaceutical field， and to provide ideas for the development of related research in China. METHODS： Using bibliometric method， relevant journals and proceeding papers from 1998 to 2017 were searched from Web of Science database （“Article” and “Proceeding Paper” as retrieval words）. Using analysis and retrieval results of ISI Web of Knowledge and its function of creating citation report， CiteSpace 5.2.R1 software was employed to draw knowledge map； quantitative statistics and qualitative analysis was conducted to summarized the research volume， co-citation， main research countries/areas， main research institutions， main researchers， research hotspots and research frontiers in this field. RESULTS： A total of 3 674 related literatures were retrieved， and the number of global published literatures in this field increased rapidly from 1998 to 2017； the dominant nations included America， China， Britain and Germany because of their large number of publications，but China showd an obvious lack of international cooperation and excellent core teams. From the researcher’s point of view， the research in this field was in a state of “partially concentrated and overall dispersed” and lacked team cooperation. The research hotspots contained important core of AI technology （machine learning algorithms） and its application in pharmaceutical field （drug discovery and design）， the classification of disease or adverse drug reaction， the establishment and optimization of pharmaceutical mode， drugs screening or pharmacodynamics prediction， the estbalishment of pharmaceutical database， etc. The recent research frontiers included “molecular docking” “machine learning” “Meta analysis” “precision medicine” “targeted therapy”， etc. CONCLUSIONS： AI applicated in pharmaceutical field is a hot research field with strong timeliness， and its application in the development of pharmaceutical industry is the general trend. However， there is still a gap between the current research level in this field in China and the international frontier research. In order to adapt to the international trend of the combination of AI technology and pharmacy， pharmacists in China should strengthen their cooperation with researchers in AI fields while doing well the basic work of experimental research and clinical trials，etc. |
| 期刊： | 2019年第30卷第4期 |
| 作者： | 凌曦，赵志刚，李新刚 |
| AUTHORS： | LING Xi，ZHAO Zhigang，LI Xingang |
| 关键字： | 人工智能；药学；知识图谱；可视化；文献计量分析；研究热点；研究前沿 |
| KEYWORDS： | Artificial intelligence； Pharmacy； Knowledge map； Visualization； Bibliometric analysis； Research hotspots； Research frontiers |
| 阅读数： | 1043 次 |
| 本月下载数： | 22 次 |
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