Machine learning models using mobile game play accurately classify children with autism

[1]  D. Wall,et al.  A Mobile Game Platform for Improving Social Communication in Children with Autism: A Feasibility Study , 2021, Applied Clinical Informatics.

[2]  T. Winograd,et al.  Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection , 2021, Scientific Reports.

[3]  E. Dorsey,et al.  Improving Access to Care: Telemedicine Across Medical Domains. , 2021, Annual review of public health.

[4]  T. Wong,et al.  Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology. , 2021, The Lancet. Digital health.

[5]  Catalin Voss,et al.  Using Crowdsourcing to Train Facial Emotion Machine Learning Models with Ambiguous Labels , 2021, ArXiv.

[6]  Dennis P. Wall,et al.  Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging , 2021, CHI Extended Abstracts.

[7]  Yordan Penev,et al.  Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder , 2020, PSB.

[8]  P. Washington,et al.  Crowdsourced feature tagging for scalable and privacy-preserved autism diagnosis , 2020, medRxiv.

[9]  Yordan Penev,et al.  Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders , 2020, ArXiv.

[10]  Yordan Penev,et al.  Feature replacement methods enable reliable home video analysis for machine learning detection of autism , 2020, Scientific Reports.

[11]  Yordan Penev,et al.  Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition , 2020, Journal of personalized medicine.

[12]  S. Vollmer,et al.  Digital Health Management During and Beyond the COVID-19 Pandemic: Opportunities, Barriers, and Recommendations , 2020, JMIR mental health.

[13]  Peter Washington,et al.  A Mobile Game for Automatic Emotion-Labeling of Images , 2020, IEEE Transactions on Games.

[14]  M. A. Kadir.  Role of telemedicine in healthcare during COVID-19 pandemic in developing countries , 2020 .

[15]  Catalin Voss,et al.  Making emotions transparent: Google Glass helps autistic kids understand facial expressions through augmented-reaiity therapy , 2020, IEEE Spectrum.

[16]  Peter Washington,et al.  The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study , 2020, JMIR mental health.

[17]  Terry Winograd,et al.  Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses , 2020, Journal of medical Internet research.

[18]  F. Garberson,et al.  Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children , 2020, Scientific Reports.

[19]  Catalin Voss,et al.  Designing a Holistic At-Home Learning Aid for Autism , 2020, ArXiv.

[20]  Haik Kalantarian,et al.  Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry. , 2019, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[21]  Svetha Venkatesh,et al.  Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety , 2019, npj Digital Medicine.

[22]  Peter Washington,et al.  Labeling images with facial emotion and the potential for pediatric healthcare , 2019, Artif. Intell. Medicine.

[23]  D. Wall,et al.  Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study , 2018, Journal of medical Internet research.

[24]  Peter Washington,et al.  Effect of Wearable Digital Intervention for Improving Socialization in Children With Autism Spectrum Disorder: A Randomized Clinical Trial , 2019, JAMA pediatrics.

[25]  K. Maschke,et al.  Patient-Centered Care, Yes; Patients As Consumers, No. , 2019, Health affairs.

[26]  Conor K. Corbin,et al.  Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study , 2019, Journal of medical Internet research.

[27]  Catalin Voss,et al.  SUPERPOWER GLASS , 2019, GETMBL.

[28]  Peter Washington,et al.  Mobile detection of autism through machine learning on home video: A development and prospective validation study , 2018, PLoS medicine.

[29]  T. Insel,et al.  Digital phenotyping: a global tool for psychiatry , 2018, World psychiatry : official journal of the World Psychiatric Association.

[30]  Peter Washington,et al.  Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism , 2018, npj Digital Medicine.

[31]  Peter Washington,et al.  A Gamified Mobile System for Crowdsourcing Video for Autism Research , 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).

[32]  Peter Washington,et al.  Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism , 2018, Applied Clinical Informatics.

[33]  Dennis P. Wall,et al.  Machine learning approach for early detection of autism by combining questionnaire and home video screening , 2017, J. Am. Medical Informatics Assoc..

[34]  Sebastien Levy,et al.  Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism , 2017, Molecular Autism.

[35]  T. Insel.  Digital Phenotyping: Technology for a New Science of Behavior. , 2017, JAMA.

[36]  Peter Washington,et al.  5.13 Design and Efficacy of a Wearable Device for Social Affective Learning in Children With Autism , 2017 .

[37]  Peter Washington,et al.  SuperpowerGlass , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[38]  Margaret Ansell,et al.  Clinical impact of early diagnosis of autism on the prognosis and parent–child relationships , 2017, Psychology research and behavior management.

[39]  A. Scarpa,et al.  Rural Trends in Diagnosis and Services for Autism Spectrum Disorder , 2017, Front. Psychol..

[40]  Peter Washington,et al.  Superpower glass: delivering unobtrusive real-time social cues in wearable systems , 2016, UbiComp Adjunct.

[41]  Peter Washington,et al.  A Wearable Social Interaction Aid for Children with Autism , 2016, CHI Extended Abstracts.

[42]  D. Wall,et al.  Use of machine learning for behavioral distinction of autism and ADHD , 2016, Translational Psychiatry.

[43]  C. Delpierre,et al.  Socioeconomic Disparities and Prevalence of Autism Spectrum Disorders and Intellectual Disability , 2015, PloS one.

[44]  D. Robins,et al.  Sociodemographic Barriers to Early Detection of Autism: Screening and Evaluation Using the M-CHAT, M-CHAT-R, and Follow-Up , 2014, Journal of Autism and Developmental Disorders.

[45]  M. Goldstein,et al.  The Patient as Consumer: Empowerment or Commodification? Currents in Contemporary Bioethics , 2015, Journal of Law, Medicine & Ethics.

[46]  D. Wall,et al.  Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning , 2015, Translational Psychiatry.

[47]  Vanessa Lobue,et al.  The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults , 2014, Front. Psychol..

[48]  D. Wall,et al.  Testing the accuracy of an observation-based classifier for rapid detection of autism risk , 2014, Translational Psychiatry.

[49]  Joseph P. Wherton,et al.  The organising vision for telehealth and telecare: discourse analysis , 2012, BMJ Open.

[50]  Todd F. DeLuca,et al.  Use of machine learning to shorten observation-based screening and diagnosis of autism , 2012, Translational Psychiatry.

[51]  R. Bernier,et al.  The Broader Autism Phenotype and Its Implications on the Etiology and Treatment of Autism Spectrum Disorders , 2011, Autism research and treatment.

[52]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[53]  P. Gosselin,et al.  Children’s Recognition and Discrimination of Fear and Disgust Facial Expressions , 2010 .

[54]  N. Minshew,et al.  The development of emotion recognition in individuals with autism. , 2009, Child development.

[55]  S. Bölte,et al.  Emotion Recognition in Children and Adolescents with Autism Spectrum Disorders , 2009, Journal of autism and developmental disorders.

[56]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[57]  T. Delbanco,et al.  Patient-centered care. , 1995, Bulletin of the New York Academy of Medicine.