Bibliometric Analysis of the Prediction of Lower Limb Injuries

Authors

Keywords:

bibliometrics, lower extremity, prediction, rehabilitation, machine learning.

Abstract

This article covers a bibliometric analysis on the prediction of lower extremity injuries. To achieve this, we started with a bibliographic search in Scopus and Web of Science databases from 2018 to 2022, using Boolean search strings. Document management software, text mining and systematic mapping were used to find global trends. Regarding the results, 4838 documents were taken into account. In the last five years there has been an interest in focusing research on words such as “machine learning”, “deep learning”, “rehabilitation”, “gait” and “electromyography”. When the focus is on the word “injury”, the network that is generated with the words “machine learning”, “injury prevention”, “prediction”, “running” and “knee” stands out. Regarding the prediction of lower extremity injuries, publications have increased over the last five years and focus their attention on patients for the application of data models based on control and rehabilitation. Focuses and networks are shown among the words “injury prevention”, “machine learning” and “rehabilitation”. Future research should focus efforts on determining the variability and specificity of machine learning processes used for prevention and control, taking into account the tissue, type of injury, area and joint affected.

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Published

2023-11-27

How to Cite

1.
Villaquiran-Hurtado AF, Burbano Fernandez MF, Celis Quinayas VM, Hoyos-Quisoboni JA. Bibliometric Analysis of the Prediction of Lower Limb Injuries. Rev. cuba. inf. cienc. salud [Internet]. 2023 Nov. 27 [cited 2025 May 9];34. Available from: https://acimed.sld.cu/index.php/acimed/article/view/2470

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