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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

104
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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Modeling in Therapy01:26

Modeling in Therapy

92
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

59
The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Related Experiment Video

Updated: Jul 12, 2025

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
09:16

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

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Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping.

Neda Abdelhamid1, Rajdeep Thind2, Heba Mohammad3

  • 1Abu Dhabi School of Management, Abu Dhabi P.O. Box 6844, United Arab Emirates.

Bioengineering (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

Early detection of autistic spectrum disorder (ASD) in toddlers is crucial. Machine learning models effectively identify key behavioral features related to communication, social interaction, and repetitive behaviors for timely ASD diagnosis.

Keywords:
autismautistic traitsclassificationdata analysisfeature selectionmachine learning

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Area of Science:

  • Neurodevelopmental disorders
  • Pediatric psychology
  • Machine learning in healthcare

Background:

  • Autistic spectrum disorder (ASD) is a neurodevelopmental condition impacting communication, social interaction, and behavior.
  • Early identification of ASD traits in toddlers is vital for timely intervention and healthcare access.
  • Existing diagnostic methods can be enhanced by identifying specific early behavioral indicators.

Purpose of the Study:

  • To identify early behavioral features indicative of ASD in toddlers.
  • To map identified features to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) neurodevelopmental criteria.
  • To propose a machine learning-based data process for ASD detection in early childhood.

Main Methods:

  • Investigated various ASD behavioral features in toddlers.
  • Employed feature selection techniques to identify relevant behavioral indicators.
  • Developed classification models, including Bayesian Network (Bayes Net) and Logistic Regression (LR), using selected features.
  • Mapped identified features to DSM-5 neurodevelopmental areas.

Main Results:

  • Cognitive features related to communication, social interactions, and repetitive behaviors were most relevant for ASD screening in toddlers.
  • Machine learning models (Bayes Net, LR) demonstrated consistent predictive accuracy using ASD behavioral data subsets.
  • The study successfully identified key behavioral markers for early ASD detection.

Conclusions:

  • Machine learning techniques are suitable for predicting Autistic Spectrum Disorder (ASD) in toddlers.
  • Early identification of specific behavioral features can significantly aid in the diagnosis and management of ASD.
  • The proposed data process offers a promising approach for early ASD detection, facilitating prompt clinical referrals and healthcare access.