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

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