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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Related Experiment Video

Updated: Jun 27, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.

Ayelet Ben-Sasson¹, Joshua Guedalia¹, Liat Nativ¹

¹Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa 3498838, Israel.

Children (Basel, Switzerland)

|April 27, 2024

Summary

Machine learning models can predict autism spectrum disorder (ASD) in infants using electronic health records. Early detection through this method identifies high-risk infants for timely intervention.

Keywords:

autism spectrum disorders development electronic health records machine learning screening

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Last Updated: Jun 27, 2025

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Published on: May 15, 2020

Area of Science:

Pediatrics
Developmental Neuroscience
Machine Learning in Healthcare

Background:

Early detection of autism spectrum disorder (ASD) is critical for effective intervention.
Current diagnostic timelines often exceed age three, delaying crucial support.
Predictive modeling using routinely collected health data offers a potential solution.

Purpose of the Study:

To develop and evaluate a machine learning model for predicting ASD diagnosis in infants.
To utilize electronic health records (EHRs) from a national screening program for prediction.
To identify key predictive factors for early ASD identification.

Main Methods:

Retrospective cohort study of 780,610 children's EHRs, including 1163 with ASD.
Gradient boosting model with 3-fold cross-validation using 100 parameters.
Shapley Additive explanation tool for feature importance quantification.

Main Results:

The model achieved an average area under the ROC curve of 0.86 (SD < 0.002).
Identified a high-risk group with 4.3-fold higher ASD incidence.
Key predictors included developmental milestone delays (language, social, motor), male gender, parental concerns, and birth/growth factors.

Conclusions:

Machine learning models can effectively predict ASD using EHR data from preventative care.
This approach facilitates early ASD screening by analyzing complex interactions of various factors.
Integration into routine wellness visits can improve timely identification and intervention for ASD.