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Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

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Title: Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review
Language: English
Authors: Kayleigh K. Hyde, Marlena N. Novack, Nicholas LaHaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead (ORCID 0000-0003-0174-7002)
Source: Review Journal of Autism and Developmental Disorders. 2019 6(2):128-146.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 19
Publication Date: 2019
Sponsoring Agency: National Science Foundation (NSF), Division of Graduate Education (DGE)
Contract Number: 1849569
Document Type: Journal Articles
Information Analyses
Descriptors: Artificial Intelligence, Autism Spectrum Disorders, Clinical Diagnosis, Intervention, Program Design, Data Collection, Data Analysis
DOI: 10.1007/s40489-019-00158-x
ISSN: 2195-7177
2195-7185
Abstract: Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1421770
Database: ERIC
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  Data: Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 19
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  Data: Journal Articles<br />Information Analyses
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Diagnosis%22">Clinical Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Design%22">Program Design</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Collection%22">Data Collection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink>
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  Data: 10.1007/s40489-019-00158-x
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  Data: 2195-7177<br />2195-7185
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  Data: Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.
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        StartPage: 128
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      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Autism Spectrum Disorders
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      – SubjectFull: Clinical Diagnosis
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      – SubjectFull: Intervention
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