Published on in Vol 5, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26760, first published .
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Journals

  1. Chi N, Washington P, Kline A, Husic A, Hou C, He C, Dunlap K, Wall D. Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study. JMIR Pediatrics and Parenting 2022;5(2):e35406 View
  2. Varma M, Washington P, Chrisman B, Kline A, Leblanc E, Paskov K, Stockham N, Jung J, Sun M, Wall D. Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods. Journal of Medical Internet Research 2022;24(2):e31830 View
  3. Washington P, Wall D. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annual Review of Biomedical Data Science 2023;6(1):211 View
  4. Gau M, Ting H, Toh T, Wong P, Woo P, Wo S, Tan G. Effectiveness of Using Artificial Intelligence for Early Child Development Screening. Green Intelligent Systems and Applications 2023;3(1):1 View
  5. Parab S, Boster J, Washington P. Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study. JMIR Formative Research 2023;7:e49898 View
  6. Jaiswal A, Washington P. Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study. JMIR Formative Research 2024;8:e52660 View
  7. Sun Y, Kargarandehkordi A, Slade C, Jaiswal A, Busch G, Guerrero A, Phillips K, Washington P. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Research Protocols 2024;13:e46493 View
  8. Jaiswal A, Kruiper R, Rasool A, Nandkeolyar A, Wall D, Washington P. Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study. JMIR Research Protocols 2024;13:e52205 View
  9. Nimitsurachat P, Washington P. Audio-Based Emotion Recognition Using Self-Supervised Learning on an Engineered Feature Space. AI 2024;5(1):195 View
  10. Washington P. A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health. Journal of Medical Internet Research 2024;26:e51138 View
  11. Kargarandehkordi A, Kaisti M, Washington P. Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study. Applied Sciences 2024;14(4):1337 View
  12. Pereira R, Mendes C, Ribeiro J, Ribeiro R, Miragaia R, Rodrigues N, Costa N, Pereira A. Systematic Review of Emotion Detection with Computer Vision and Deep Learning. Sensors 2024;24(11):3484 View

Books/Policy Documents

  1. Ilijoski B, Ackovska N. ICT Innovations 2023. Learning: Humans, Theory, Machines, and Data. View