Dhombres Ferdinand, Bodenreider Olivier
National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Stud Health Technol Inform. 2017;245:853-857.
The objective is to automatically identify trends in Fetal Medicine over the past 10 years through a bibliometric analysis of articles published in Prenatal Diagnosis, using text mining techniques. We processed 2,423 full-text articles published in Prenatal Diagnosis between 2006 and 2015. We extracted salient terms, calculated their frequencies over time, and established evolution profiles for terms, from which we derived falling, stable, and rising trends. We identified 618 terms with a falling trend, 2,142 stable terms, and 839 terms with a rising trend. Terms with increasing frequencies include those related to statistics and medical study design. The most recent of these terms reflect the new opportunities of next-generation sequencing. Many terms related to cytogenetics exhibit a falling trend. A bibliometric analysis based on text mining effectively supports identification of trends over time. This scalable approach is complementary to analyses based on metadata or expert opinion.
目的是通过对发表在《产前诊断》上的文章进行文献计量分析,利用文本挖掘技术自动识别过去10年胎儿医学的发展趋势。我们处理了2006年至2015年期间发表在《产前诊断》上的2423篇全文文章。我们提取了显著术语,计算了它们随时间的频率,并建立了术语的演变概况,从中得出下降、稳定和上升趋势。我们识别出618个呈下降趋势的术语、2142个稳定术语和839个呈上升趋势的术语。频率增加的术语包括与统计学和医学研究设计相关的术语。这些术语中最新的反映了下一代测序的新机遇。许多与细胞遗传学相关的术语呈下降趋势。基于文本挖掘的文献计量分析有效地支持了对随时间变化趋势的识别。这种可扩展的方法是对基于元数据或专家意见的分析的补充。