Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can deter pediatric screening. The Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) is a widely used screener but requires follow-up questions and error-prone human scoring and interpretation. We consider an automated machine learning (ML) method for overcoming barriers to ASD screening, specifically using the feedforward neural network (fNN).

Luke E. K. Achenie

Angela Scarpa

Reina S. Factor

Tao Wang

Diana L. Robins

D. Scott McCrickard

Journal of Developmental & Behavioral Pediatrics

Journal of Developmental & Behavioral Pediatrics (JDBP) is a leading resource for clinicians, teachers, and researchers involved in pediatric healthcare and child development. This important journal covers some of the most challenging issues affecting child development and behavior.

The Journal of Developmental and Behavioral Pediatrics (JDBP) is a vital resource for clinicians, educators, and researchers involved in pediatric healthcare and child development. This important journal addresses some of the most challenging issues affecting child development and behavior.

《Journal of Developmental and Behavioral Pediatrics》是一个重要的资源，专为参与儿童医疗和发展研究的临床医生、教师和研究人员而设。该期刊涵盖了影响儿童发展和行为的一些最具挑战性的问题。

Journal of Developmental and Behavioral Pediatrics

A Machine Learning Strategy for Autism Screening in Toddlers

The ML method was comparable to the M-CHAT-R with follow-up items in accuracy of ASD diagnosis while using fewer items, and may be a beneficial tool in implementing automatic, efficient scoring that negates the need for labor-intensive following-up and circumvents human error.

Category	Quartile
医学	3
医学, 行为科学	3
医学, 儿科	3
医学, 心理学发育	4

A Machine Learning Strategy for Autism Screening in Toddlers

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Journal Info

Category	Quartile
PSYCHOLOGY, DEVELOPMENTAL	3