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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7340

    Título
    Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
    Autor
    Sáiz Manzanares, María ConsueloAutoridad UBU Orcid
    Marticorena Sánchez, RaúlAutoridad UBU Orcid
    Arnaiz González, ÁlvarAutoridad UBU Orcid
    Publicado en
    International Journal of Environmental Research and Public Health. 2022, V. 19, n. 11, 6558
    Editorial
    MDPI
    Fecha de publicación
    2022-05
    DOI
    10.3390/ijerph19116558
    Resumo
    Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0–6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users’ developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs.
    Palabras clave
    Early care
    Web application
    Machine learning techniques
    Precision therapeutic program
    Personalized intervention
    Disabilities
    Materia
    Psicología
    Psychology
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/7340
    Versión del editor
    https://doi.org/10.3390/ijerph19116558
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    Saiz-erph_2022.pdf
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