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

Children and Youth with Disabilities (48) Clinical Informatics (912) Autism Spectrum Disorder (ASD) (96) Machine Learning (1383) Artificial Intelligence (1194) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (91)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35406, first published .
Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study

Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study

Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study

Authors of this article:

Nathan A Chi1 Author Orcid Image ;   Peter Washington2 Author Orcid Image ;   Aaron Kline1 Author Orcid Image ;   Arman Husic1 Author Orcid Image ;   Cathy Hou3 Author Orcid Image ;   Chloe He4 Author Orcid Image ;   Kaitlyn Dunlap1 Author Orcid Image ;   Dennis P Wall1, 4, 5 Author Orcid Image
Article Authors Cited by (18) Tweetations (21) Metrics

Journals

  1. Sleiman E, Mutlu O, Surabhi S, Husic A, Kline A, Washington P, Wall D. Deep Learning-Based Autism Spectrum Disorder Detection Using Emotion Features From Video Recordings (Preprint). JMIR Biomedical Engineering 2022 View
  2. Banerjee A, Mutlu O, Kline A, Surabhi S, Washington P, Wall D. Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study. JMIR Formative Research 2023;7:e39917 View
  3. Liu X, Zhao W, Qi Q, Luo X. A Survey on Autism Care, Diagnosis, and Intervention Based on Mobile Apps: Focusing on Usability and Software Design. Sensors 2023;23(14):6260 View
  4. Idrisoglu A, Dallora A, Anderberg P, Berglund J. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. Journal of Medical Internet Research 2023;25:e46105 View
  5. 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
  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. 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
  8. Sfakianaki A, Nicolaidis K, Kafentzis G. Temporal, spectral and amplitude characteristics of the Greek fricative /s/ in hearing-impaired and normal-hearing speech. Clinical Linguistics & Phonetics 2024:1 View
  9. Washington P. A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health. Journal of Medical Internet Research 2024;26:e51138 View
  10. Toki E, Pange J, Tatsis G, Plachouras K, Tsoulos I. Utilizing Constructed Neural Networks for Autism Screening. Applied Sciences 2024;14(7):3053 View
  11. Rogers H, Hseu A, Kim J, Silberholz E, Jo S, Dorste A, Jenkins K. Voice as a Biomarker of Pediatric Health: A Scoping Review. Children 2024;11(6):684 View
  12. Lin K, Washington P. Multimodal deep learning for dementia classification using text and audio. Scientific Reports 2024;14(1) View

Books/Policy Documents

  1. Ramos V, Mondéjar T, Ferrández A, Peral J, Gil D, Mora H. Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). View
  2. Mouad E, Hanae B, Khalid M. Engineering Applications of Neural Networks. View