Cargando…

Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years

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 ther...

Descripción completa

Detalles Bibliográficos
Autores principales: Sáiz-Manzanares, María Consuelo, Marticorena-Sánchez, Raúl, Arnaiz-González, Álvar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180398/
https://www.ncbi.nlm.nih.gov/pubmed/35682142
http://dx.doi.org/10.3390/ijerph19116558
_version_ 1784723508610203648
author Sáiz-Manzanares, María Consuelo
Marticorena-Sánchez, Raúl
Arnaiz-González, Álvar
author_facet Sáiz-Manzanares, María Consuelo
Marticorena-Sánchez, Raúl
Arnaiz-González, Álvar
author_sort Sáiz-Manzanares, María Consuelo
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9180398
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91803982022-06-10 Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years Sáiz-Manzanares, María Consuelo Marticorena-Sánchez, Raúl Arnaiz-González, Álvar Int J Environ Res Public Health Article 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. MDPI 2022-05-27 /pmc/articles/PMC9180398/ /pubmed/35682142 http://dx.doi.org/10.3390/ijerph19116558 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sáiz-Manzanares, María Consuelo
Marticorena-Sánchez, Raúl
Arnaiz-González, Álvar
Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
title Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
title_full Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
title_fullStr Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
title_full_unstemmed Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
title_short Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years
title_sort improvements for therapeutic intervention from the use of web applications and machine learning techniques in different affectations in children aged 0–6 years
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180398/
https://www.ncbi.nlm.nih.gov/pubmed/35682142
http://dx.doi.org/10.3390/ijerph19116558
work_keys_str_mv AT saizmanzanaresmariaconsuelo improvementsfortherapeuticinterventionfromtheuseofwebapplicationsandmachinelearningtechniquesindifferentaffectationsinchildrenaged06years
AT marticorenasanchezraul improvementsfortherapeuticinterventionfromtheuseofwebapplicationsandmachinelearningtechniquesindifferentaffectationsinchildrenaged06years
AT arnaizgonzalezalvar improvementsfortherapeuticinterventionfromtheuseofwebapplicationsandmachinelearningtechniquesindifferentaffectationsinchildrenaged06years