2026-05-23T07:32:46Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73402023-03-21T10:52:15Zcom_10259_5841com_10259_5086com_10259_2604com_10259_4219col_10259_5842col_10259_4220

Sáiz Manzanares, María Consuelo Marticorena Sánchez, Raúl Arnaiz González, Álvar 2023-01-26T13:36:51Z 2023-01-26T13:36:51Z 2022-05 http://hdl.handle.net/10259/7340 10.3390/ijerph19116558 1660-4601 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. The development of the “eEarly Care” and “Therapeutic intervention programs” web applications has been financed by FEDER FOUNDS: VI Edition of the Call for Proofs of Concept: Impulse for the valorization and marketing of research results (2018–2019), VII Edition of the Call for Proofs of Concept: Impulse for the valorization and marketing of research results (2019–2020) and VII Edition of the Call for Proofs of Concept: Impulse for the valorization and marketing of research results (2020–2021), all managed by the JUNTA DE CASTILLA Y LÉON (SPAIN). Currently, training in the use of these web applications has been co-funded by the EUROPEAN UNION in the e-EarlyCare-T research project No. 2021-1-ES01-KA220-SCH-9A787316. application/pdf eng MDPI International Journal of Environmental Research and Public Health. 2022, V. 19, n. 11, 6558 https://doi.org/10.3390/ijerph19116558 info:eu-repo/grantAgreement/EC/Erasmus+/2021-1-ES01-KA220-SCH-9A787316/EU/Specialized and updated training on supporting advance technologies for early childhood education and care professionals and graduates/eEarlyCare-T/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Early care Web application Machine learning techniques Precision therapeutic program Personalized intervention Disabilities Psicología Informática Psychology Computer science Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion International Journal of Environmental Research and Public Health 19 11