Pediatric literature trends: high-level analysis using text-mining

Background: 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. Methods: We retrieved all available MEDLINE/PubMed annual data sets until December 31, 2018. Included studies were categorized into topics using text-mining. Results: 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. Conclusions: 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. Impact: 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.