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...
Autores principales: | , , |
---|---|
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 |