A bibliometric analysis of natural language processing in medical research.
A bibliometric analysis of natural language processing in medical research.
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医学研究中自然语言处理的文献计量分析
DOI：
10.1186/s12911-018-0594-x
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发表时间：
2018-03-22
影响因子：
3.5
通讯作者：
Hao T
中科院分区：
文献类型：
--
作者：
Chen X;Xie H;Wang FL;Liu Z;Xu J;Hao T
背景自然语言处理 (NLP) 在推动医学发展方面发挥着越来越重要的作用。 NLP方法及其在医学信息处理应用方面的研究成果丰富。深入分析了解NLP赋能的医学研究领域的最新发展具有重要意义。然而，对该领域研究现状的研究有限。因此，本研究旨在定量评估NLP在医学研究领域的学术成果。方法我们对2007-2016年从PubMed检索的NLP授权的医学研究出版物进行了文献计量分析。分析主要集中在三个方面。首先，采用统计分析方法获得文献分布特征。其次，采用网络分析方法揭示科学合作关系。最后，利用亲和力传播聚类方法反映主题发现和演变。结果10年间发表了1405篇基于NLP的医学研究出版物，年均增长率为18.39%。 10个生产力最高的出版物来源合计占出版物总量的50%以上。美国的出版物数量最多。国家出版物与人均 GDP 之间存在中等显着相关性。约书亚·C 丹尼 (Denny, Joshua C) 是生产力最高的作者。梅奥诊所 (Mayo Clinic) 是生产力最高的附属机构。 2016年全年联属和联国率分别达到64.04%和15.79%。确定了 10 个主要主题领域，包括计算生物学、术语挖掘、信息提取、文本分类、作为数据源的社交媒体、信息检索等。结论对 NLP 授权的医学研究出版物进行了文献计量分析，以揭示最新的研究现状。研究结果可以帮助相关研究人员，特别是新研究者系统地了解研究进展、寻找科学合作伙伴、优化研究课题选择以及监测新的科学或技术活动。
BackgroundNatural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field.MethodsWe conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007–2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method.ResultsThere were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country’s publications and GDP per capita was revealed.Denny, Joshua Cwas the most productive author.Mayo Clinicwas the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified includingComputational biology,Terminology mining,Information extraction,Text classification,Social medium as data source,Information retrieval, etc.ConclusionsA bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.
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影响因子：
3.7
作者：
Albers DJ;Elhadad N;Tabak E;Perotte A;Hripcsak G
通讯作者：
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影响因子：
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作者：
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DOI：
10.1515/jaiscr-2015-0032
发表时间：
2015-10-01
影响因子：
2.8
作者：
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通讯作者：
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影响因子：
5.8
作者：
Bodenhofer, Ulrich;Kothmeier, Andreas;Hochreiter, Sepp
通讯作者：
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