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 |
| 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 |
| 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. |
|---|---|
| ISSN: | 2195-7177 2195-7185 |
| DOI: | 10.1007/s40489-019-00158-x |
