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

Pediatric Research volume 90, pages 212–215 (2021)Cite this article

A Correction to this article was published on 21 July 2021

This article has been updated

Abstract

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.

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Fig. 1: Citations/publications ratio per country.
Fig. 2: Number of articles per type of article.
Fig. 3: Citations/publications ratio per article type.
Fig. 4: Number of publications per topic over the 2 decades.
Fig. 5: Citations/publications ratio per topic.

Change history

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Acknowledgements

We would like to thank MS. Amy Zhong at Icahn School of Medicine at Mount Sinai, New York for her expert assistance with the design of the tables and figure. No external funding was used for this research.

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Affiliations

  1. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Sarina Levy-Mendelovich, Yiftach Barbash, Daniella Levy-Erez, Raz Somech, Shelly Soffer & Eyal Klang

  2. The Israeli National Hemophilia Center and Thrombosis Unit and Amalia Biron Research Institute of Thrombosis and Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel

    Sarina Levy-Mendelovich

  3. The Sheba Talpiot Medical Leadership Program, Chaim Sheba Medical Center, Tel Hashomer, Israel

    Sarina Levy-Mendelovich & Eyal Klang

  4. Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel

    Yiftach Barbash

  5. Department of Pathophysiology, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

    Ivan Budnik & Shelly Soffer

  6. Division of Nephrology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Daniella Levy-Erez & Susan Furth

  7. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Daniella Levy-Erez & Susan Furth

  8. Pediatric Nephrology unit, Schnieder Pediatric hospital, Petach Tiqva, Israel

    Daniella Levy-Erez

  9. Pediatric Department A and the Immunology Service, “Jeffrey Modell Foundation Center Edmond and Lily Safra Children’s Hospital”, Sheba Medical center, Ramat Gan, Israel

    Raz Somech

  10. Institute for Healthcare Delivery Science (IHDS), Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eyal Klang

Authors
  1. Sarina Levy-Mendelovich
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  2. Yiftach Barbash
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  3. Ivan Budnik
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  4. Daniella Levy-Erez
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  5. Raz Somech
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  6. Shelly Soffer
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  7. Susan Furth
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  8. Eyal Klang
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Contributions

S.L.-M., E.K., and Y.B. contributed to conception and design and acquisition of data. Y.B., I.B., S.S., S.F., R.S., and D.L.-E. contributed to interpretation of data. S.L.-M., E.K., and D.L.-E. drafted the article, revised it critically for important intellectual content, and approved the version to be published. I.B., S.F., and R.S. revised the article critically for important intellectual content.

Corresponding author

Correspondence to Sarina Levy-Mendelovich.

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The authors declare no competing interests.

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No patient consent was needed in this study.

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The original online version of this article was revised: The captions for Figures 1, 3, and 5 have been corrected.

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Levy-Mendelovich, S., Barbash, Y., Budnik, I. et al. Pediatric literature trends: high-level analysis using text-mining. Pediatr Res 90, 212–215 (2021). https://doi.org/10.1038/s41390-021-01415-8

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  • DOI: https://doi.org/10.1038/s41390-021-01415-8

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