Pediatric literature trends: high-level analysis using text-mining.
Journal
Pediatric research
ISSN: 1530-0447
Titre abrégé: Pediatr Res
Pays: United States
ID NLM: 0100714
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
22
12
2020
accepted:
16
01
2021
revised:
13
01
2021
pubmed:
19
3
2021
medline:
9
2
2022
entrez:
18
3
2021
Statut:
ppublish
Résumé
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.
Sections du résumé
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.
Identifiants
pubmed: 33731817
doi: 10.1038/s41390-021-01415-8
pii: 10.1038/s41390-021-01415-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
212-215Commentaires et corrections
Type : CommentIn
Type : ErratumIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.
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