Análisis bibliométrico de la predicción de lesiones en miembros inferiores
Palabras clave:
bibliometría, extremidad inferior, predicción, rehabilitación, aprendizaje automático.Resumen
Este artículo realiza un análisis bibliométrico acerca de la predicción de lesiones en extremidades inferiores. Para ello se parte de una búsqueda bibliográfica en las bases de datos Scopus y Web of Science durante el período 2018-2022, utilizando cadenas booleanas de búsqueda. Se empleó un software de gestión documental, la minería de texto y un mapeo sistemático para encontrar las tendencias a nivel mundial. En cuanto a los resultados, se tuvieron en cuenta 4838 documentos. En los últimos cinco años existe un interés por centrar las investigaciones en palabras como “machine learning”, “deep learning”, “rehabilitation”, “gait” y “electromyography”. Cuando el foco es la palabra “injury” sobresale la red que se genera con las palabras: “machine learning”, “injury prevention”, “prediction”, “running” y “knee”. En lo que concierne a la predicción de lesiones de extremidades inferiores, las publicaciones han aumentado durante los últimos cinco años y centran su atención en los pacientes para la aplicación de modelos de datos en función del control y la rehabilitación. Se muestran focos de atención y redes entre las palabras “injury prevention”, “machine learning” y “rehabilitation”. Las investigaciones futuras deben centrar sus esfuerzos en determinar la variabilidad y la especificidad de los procesos de aprendizaje automático utilizados para la prevención y el control, teniendo en cuenta el tejido, el tipo de lesión, la zona y la articulación afectada.
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