Special Issue "Artificial Intelligence for Children, Teenagers and People with Health Problems"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Children's Health".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. María Consuelo Sáiz Manzanares
Website SciProfiles
Guest Editor
Departamento de Ciencias de la Salud, Universidad de Burgos, Burgos, Spain
Interests: blended learning; metacognition; artificial intelligence; data mining; early care
Dr. César Ignacio García Osorio
Website
Guest Editor
Departamento de Ingeniería Informática, Universidad de Burgos, Burgos, Spain
Interests: data mining; data visualization; ensemble learning; big data; bioinformatics learning
Dr. Luis Jorge Martín Antón
Website
Guest Editor
Departamento de Psicología, Universidad de Valladolid, Valladolid, Spain
Interests: learning strategies; effective learning; secondary schooling; university

Special Issue Information

Dear Colleagues,

In recent years, the application of technology and Machine Learning techniques to Health Sciences has been increasing. Technological resources help both the detection (diagnosis) of different health problems (physical, psychic, or sensory) and their corresponding intervention to overcome them (treatment). This is very significant for the prevention (primary and secondary) of possible health problems or pathologies. Therefore, it is important to promote research in this area.

This Special Issue aims to collect research experiences that are being developed in the field of health and education, involving the use of technological resources and artificial intelligence and/or machine learning techniques aimed at providing personalized responses to various problems, including developmental problems, for children and teenagers. The final objective of these studies will be to achieve more precise diagnosis and/or interventions and improve the quality of life of the concerned children and teenagers in different areas and domains (social, family, personal autonomy, learning, etc.). Papers in which computer technologies (such as Artificial Intelligence, Data Mining, and Machine Learning) are applied to treat medical, psychological and pedagogical problems will be considered for publication. Likewise, quantitative, qualitative, or mixed methodological designs will be also of interest.

Dr. María Consuelo Sáiz Manzanares
Dr. César Ignacio García Osorio
Dr. Luis Jorge Martín Antón
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • social inclusion
  • quality of life
  • early care
  • computer solutions

Published Papers (3 papers)

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Research

Open AccessArticle
Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment
Int. J. Environ. Res. Public Health 2020, 17(18), 6873; https://doi.org/10.3390/ijerph17186873 - 20 Sep 2020
Abstract
Youths experiencing homelessness (YEH) often cycle between various sheltering locations including spending nights on the streets, in shelters and with others. Few studies have explored the patterns of daily sheltering over time. A total of 66 participants completed 724 ecological momentary assessments that [...] Read more.
Youths experiencing homelessness (YEH) often cycle between various sheltering locations including spending nights on the streets, in shelters and with others. Few studies have explored the patterns of daily sheltering over time. A total of 66 participants completed 724 ecological momentary assessments that assessed daily sleeping arrangements. Analyses applied a hypothesis-generating machine learning algorithm (component-wise gradient boosting) to build interpretable models that would select only the best predictors of daily sheltering from a large set of 92 variables while accounting for the correlated nature of the data. Sheltering was examined as a three-category outcome comparing nights spent literally homeless, unstably housed or at a shelter. The final model retained 15 predictors. These predictors included (among others) specific stressors (e.g., not having a place to stay, parenting and hunger), discrimination (by a friend or nonspecified other; due to race or homelessness), being arrested and synthetic cannabinoids use (a.k.a., “kush”). The final model demonstrated success in classifying the categorical outcome. These results have implications for developing just-in-time adaptive interventions for improving the lives of YEH. Full article
Open AccessArticle
Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19
Int. J. Environ. Res. Public Health 2020, 17(15), 5618; https://doi.org/10.3390/ijerph17155618 - 04 Aug 2020
Cited by 1
Abstract
The use of advanced learning technologies in a learning management system (LMS) can greatly assist learning processes, especially when used in university environments, as they promote the development of Self-Regulated learning, which increases academic performance and student satisfaction towards personal learning. One of [...] Read more.
The use of advanced learning technologies in a learning management system (LMS) can greatly assist learning processes, especially when used in university environments, as they promote the development of Self-Regulated learning, which increases academic performance and student satisfaction towards personal learning. One of the most innovative resources that an LMS may have is an Intelligent Personal Assistant (IPA). We worked with a sample of 109 third-grade students following Health Sciences degrees. The aims were: (1) to verify whether there will be significant differences in student access to the LMS, depending on use versus non-use of an IPA. (2) To verify whether there will be significant differences in student learning outcomes depending on use versus non-use of an IPA. (3) To verify whether there will be significant differences for student satisfaction with teaching during the COVID-19 pandemic, depending on use versus non-use of an IPA. (4) To analyze student perceptions of the usefulness of an IPA in the LMS. We found greater functionality in access to the LMS and satisfaction with teaching, especially during the health crisis, in the group of students who had used an IPA. However, both the expansion of available information and the usability of the features embedded in an IPA are still challenging issues. Full article
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Open AccessArticle
Improvement of Attention in Elementary School Students through Fixation Focus Training Activity
Int. J. Environ. Res. Public Health 2020, 17(13), 4780; https://doi.org/10.3390/ijerph17134780 - 03 Jul 2020
Abstract
The attentional problems of school children are a crucial topic due to abundant information in this digital era. There are five attention dimensions for children: focused attention, sustained attention, selective attention, alternating attention, and divided attention. Focused training is a traditional method of [...] Read more.
The attentional problems of school children are a crucial topic due to abundant information in this digital era. There are five attention dimensions for children: focused attention, sustained attention, selective attention, alternating attention, and divided attention. Focused training is a traditional method of improving attention ability. Subjects are required to focus on a fixed point for an extensive period without blinking and to perceive small objects as large. This study investigates which types of attention indicators are influenced by focus training. Eighty-two grade five and six elementary school students (45 experiment group, 37 control group) were involved. The experiment group underwent focus training for 12 weeks. The training was conducted once per week, and the Attention Scales for Elementary School Children were used before and after the training to examine the children’s attention. The percentile rank scores of five attention dimensions and the total attention scale were evaluated. The results gave difference data, defined as post-test results minus the pretest results, where significant differences occurred for the total scale (p < 0.05), focused attention (p < 0.05), and selective attention (p < 0.01). Participants also noted that the training helped them improve concentration during school lessons (54.15%), fall asleep (29.1%), and relax the body (8.4%). Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Gender, age, year of course and faculty: factors influencing the risk of eating disorders
Authors: Escolar-Llamazares, M. C.; Martínez Martín, M. A.; Medina Gómez, M. B.; González Alonso, M. Y.; Mercado Val, E.; Lara Ortega, F.
Affiliation: Departamento de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain

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