Machine learning approach for early detection of autism by combining questionnaire and home video screening.

Journal: Journal of the American Medical Informatics Association : JAMIA

PMID: 29741630

Abstract

BACKGROUND: Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool.

Authors

Halim Abbas

Cognoa Inc., Palo Alto, CA, USA www.linkedin.com/in/halimabbas.
Ford Garberson

Cognoa Inc., Palo Alto, CA, USA www.linkedin.com/in/halimabbas.
Eric Glover
Dennis P Wall

Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California; Department of Psychiatry and Behavioral Sciences (by courtesy), Stanford University, Stanford, California. Electronic address: dpwall@stanford.edu.

Keywords

Algorithms Autistic Disorder Child, Preschool Early Diagnosis Humans Machine Learning Methods ROC Curve Surveys and Questionnaires Videotape Recording

External Resources

View on PubMed Access via DOI PubMed (29741630)

Machine learning approach for early detection of autism by combining questionnaire and home video screening.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals