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Building Language Resources for Exploring Autism Spectrum Disorders.

Julia Parish-Morris1, Christopher Cieri2, Mark Liberman2

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|September 1, 2018
PubMed
Summary
This summary is machine-generated.

Researchers are developing new tools using human language technologies (HLTs) to improve autism spectrum disorder (ASD) screening and diagnosis. This involves creating a dataset of annotated language samples to advance early detection and personalized interventions for ASD.

Keywords:
annotationautism spectrum disordercollectiondata distributionlanguage resourcesquality control

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Area of Science:

  • Neurodevelopmental Disorders
  • Computational Linguistics
  • Clinical Psychology

Background:

  • Autism spectrum disorder (ASD) diagnosis relies on subjective clinical judgment, necessitating more objective and accessible screening methods.
  • Human language technologies (HLTs) offer potential for automating diagnostic processes and improving the efficiency of ASD assessment.
  • Current diagnostic tools for ASD can be costly and lack widespread accessibility, highlighting the need for innovative solutions.

Purpose of the Study:

  • To introduce a novel resource comprising 100 annotated language samples for ASD research.
  • To assess the feasibility of digitizing and sharing sensitive clinical data for HLT-based ASD analysis.
  • To lay the groundwork for an international repository of annotated language samples to advance ASD research.

Main Methods:

  • Collected 100 semi-structured, 20-minute English language samples from individuals.
  • Annotated samples with child age, sex, IQ, autism symptom severity, and diagnostic classification.
  • Evaluated the process of digitizing and preparing clinical samples for data sharing and analysis.

Main Results:

  • Successfully created a valuable dataset of 100 annotated language samples.
  • Identified challenges and best practices for digitizing and processing sensitive clinical data.
  • Demonstrated the feasibility of creating a shared resource for ASD language research.

Conclusions:

  • The developed resource and methodology support the advancement of HLTs for ASD screening and diagnosis.
  • Establishing an international repository of annotated language samples can accelerate research and improve early detection.
  • This work has the potential to enhance remote screening, personalize interventions, and benefit individuals with ASD and their families.