College of Economics, Jinan University, Guangzhou, China.
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, Hong Kong, Special Administrative Region of China.
BMC Med Inform Decis Mak. 2018 Mar 22;18(Suppl 1):14. doi: 10.1186/s12911-018-0594-x.
Natural 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.
We 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.
There 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 C was the most productive author. Mayo Clinic was 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 including Computational biology, Terminology mining, Information extraction, Text classification, Social medium as data source, Information retrieval, etc. CONCLUSIONS: A 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.
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。现已有大量关于医学信息处理的 NLP 方法和应用的研究成果。对 NLP 赋能的医学研究领域的最新发展进行深入分析具有重要意义。然而，目前可用于检查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产出。
我们对 2007-2016 年间从 PubMed 检索到的 NLP 赋能的医学研究文献进行了文献计量分析。分析集中在三个方面。首先，使用统计分析方法获取文献分布特征。其次，使用网络分析方法揭示科学合作关系。最后，使用亲和传播聚类方法反映主题发现和演变。
在这 10 年中，共发表了 1405 篇 NLP 赋能的医学研究论文，年均增长率为 18.39%。前 10 位最具生产力的出版物来源贡献了超过 50%的总出版物。美国发表的论文数量最多。揭示了国家出版物与人均 GDP 之间存在中度显著相关性。最有生产力的作者是 Joshua C. Denny。最有生产力的机构是 Mayo 诊所。2016 年，年度合作机构和合作国家的比例分别达到 64.04%和 15.79%。确定了 10 个主要的大主题领域，包括计算生物学、术语挖掘、信息提取、文本分类、社交媒体作为数据源、信息检索等。
本文通过对 NLP 赋能的医学研究文献进行文献计量分析，揭示了其近期的研究现状。研究结果可以帮助相关研究人员，尤其是新手，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，并监测新的科学或技术活动。
