This paper is in the following e-collection/theme issue:

Serious Games for Health and Medicine (683) Games for Cognitive Assessment (61) Games and Gamification for Health (650) Children and Youth with Disabilities (53) Theoretical Foundations and Frameworks on Games and Gamification (41) Game Design and Efficacy of Game Elements (86) Machine Learning (1465) Disabilities (116) Diagnostic Tools in Mental Health (242) Game Development (57) Digital Biomarkers and Digital Phenotyping (191) Developmental Problems (47)

Published on in Vol 9, No 2 (2021): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23130, first published .
A Prediction Model for Detecting Developmental Disabilities in Preschool-Age Children Through Digital Biomarker-Driven Deep Learning in Serious Games: Development Study

A Prediction Model for Detecting Developmental Disabilities in Preschool-Age Children Through Digital Biomarker-Driven Deep Learning in Serious Games: Development Study

A Prediction Model for Detecting Developmental Disabilities in Preschool-Age Children Through Digital Biomarker-Driven Deep Learning in Serious Games: Development Study

Authors of this article:

Ho Heon Kim1 Author Orcid Image ;   Jae Il An1 Author Orcid Image ;   Yu Rang Park1 Author Orcid Image
Article Authors Cited by (15) Tweetations (3) Metrics

Journals

  1. Jeong H, Jeong Y, Park Y, Kim K, Park J, Kang D. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. DIGITAL HEALTH 2022;8:205520762211366 View
  2. Bowe A, Lightbody G, Staines A, Murray D. Big data, machine learning, and population health: predicting cognitive outcomes in childhood. Pediatric Research 2023;93(2):300 View
  3. Park Y, Go T, Hong S, Kim S, Han J, Kang Y, Kang D. Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review. Yonsei Medical Journal 2022;63(Suppl):S43 View
  4. Petsani D, Konstantinidis E, Katsouli A, Zilidou V, Dias S, Hadjileontiadis L, Bamidis P. Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study. JMIR Serious Games 2022;10(3):e34768 View
  5. Li S, Liu Y, Kumar V. Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation. Computational Intelligence and Neuroscience 2022;2022:1 View
  6. Toki E, Tatsis G, Tatsis V, Plachouras K, Pange J, Tsoulos I. Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication. Mathematics 2023;11(7):1643 View
  7. Toki E, Tatsis G, Tatsis V, Plachouras K, Pange J, Tsoulos I. Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders. Signals 2023;4(2):401 View
  8. Zhao Y, Gao W, Ku S. Optimization of the game improvement and data analysis model for the early childhood education major via deep learning. Scientific Reports 2023;13(1) View
  9. Chun S, Jang S, Kim J, Ko C, Lee J, Hong J, Park Y. Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study. JMIR Formative Research 2024;8:e51996 View
  10. Toki E, Tatsis G, Pange J, Tsoulos I. Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution. Applied Sciences 2023;14(1):305 View
  11. Toki E, Pange J, Tatsis G, Plachouras K, Tsoulos I. Utilizing Constructed Neural Networks for Autism Screening. Applied Sciences 2024;14(7):3053 View
  12. Yow A, Wong D, Zhang Y, Menke C, Wolleschensky R, Török P. Artificial intelligence in optical lens design. Artificial Intelligence Review 2024;57(8) View
  13. Toki E, Zakopoulou V, Tatsis G, Pange J. Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial. JMIR Formative Research 2024;8:e53465 View
  14. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View

Books/Policy Documents

  1. Toki E, Zakopoulou V, Tatsis G, Plachouras K, Pange J. Open Science in Engineering. View