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dc.contributor.authorSáiz Manzanares, María Consuelo 
dc.contributor.authorMarticorena Sánchez, Raúl 
dc.contributor.authorArnaiz González, Álvar 
dc.date.accessioned2023-01-26T13:36:51Z
dc.date.available2023-01-26T13:36:51Z
dc.date.issued2022-05
dc.identifier.urihttp://hdl.handle.net/10259/7340
dc.description.abstractTechnological 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.en
dc.description.sponsorshipThe 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofInternational Journal of Environmental Research and Public Health. 2022, V. 19, n. 11, 6558es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectEarly careen
dc.subjectWeb applicationen
dc.subjectMachine learning techniquesen
dc.subjectPrecision therapeutic programen
dc.subjectPersonalized interventionen
dc.subjectDisabilitiesen
dc.subject.otherPsicologíaes
dc.subject.otherPsychologyen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleImprovements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Yearsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/ijerph19116558es
dc.identifier.doi10.3390/ijerph19116558
dc.relation.projectIDinfo: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/
dc.identifier.essn1660-4601
dc.journal.titleInternational Journal of Environmental Research and Public Healthen
dc.volume.number19es
dc.issue.number11es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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