Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
The Israeli National Hemophilia Center and Thrombosis Unit and Amalia Biron Research Institute of Thrombosis and Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel.
Pediatr Res. 2021 Jul;90(1):212-215. doi: 10.1038/s41390-021-01415-8. Epub 2021 Mar 17.
Pediatric research is a diverse field that is constantly growing. Current machine learning advancements have prompted a technique termed text-mining. In text-mining, information is extracted from texts using algorithms. This technique can be applied to analyze trends and to investigate the dynamics in a research field. We aimed to use text-mining to provide a high-level analysis of pediatric literature over the past two decades.
We retrieved all available MEDLINE/PubMed annual data sets until December 31, 2018. Included studies were categorized into topics using text-mining.
Two hundred and twenty-five journals were categorized as Pediatrics, Perinatology, and Child Health based on Scimago ranking for medicine journals. We included 201,141 pediatric papers published between 1999 and 2018. The most frequently cited publications were clinical guidelines and meta-analyses. We found that there is a shift in the trend of topics. Epidemiological studies are gaining more publications while other topics are relatively decreasing.
The topics in pediatric literature have shifted in the past two decades, reflecting changing trends in the field. Text-mining enables analysis of trends in publications and can serve as a high-level academic tool.
Text-mining enables analysis of trends in publications and can serve as a high-level academic tool. This is the first study using text-mining techniques to analyze pediatric publications. Our findings indicate that text-mining techniques enable better understanding of trends in publications and should be implemented when analyzing research.
儿科研究是一个不断发展的多元化领域。当前机器学习的进步促使了一种被称为文本挖掘的技术。在文本挖掘中，使用算法从文本中提取信息。这项技术可以应用于分析趋势并研究研究领域的动态。我们旨在使用文本挖掘对过去二十年的儿科文献进行高层次分析。
我们检索了截至 2018 年 12 月 31 日所有可用的 MEDLINE/PubMed 年度数据集。使用文本挖掘对纳入的研究进行分类。
根据医学期刊 Scimago 排名，225 种期刊被归类为儿科学、围产医学和儿童健康。我们纳入了 1999 年至 2018 年间发表的 201,141 篇儿科论文。引用最多的出版物是临床指南和荟萃分析。我们发现主题趋势发生了变化。流行病学研究的出版物越来越多，而其他主题则相对减少。
在过去的二十年中，儿科文献的主题发生了变化，反映了该领域的变化趋势。文本挖掘可以分析出版物的趋势，并且可以作为一种高级学术工具。
文本挖掘可以分析出版物的趋势，并且可以作为一种高级学术工具。这是首次使用文本挖掘技术分析儿科出版物的研究。我们的研究结果表明，文本挖掘技术可以更好地理解出版物的趋势，在分析研究时应加以实施。
