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Autism Screening Using Deep Embedding Representation

  • Haishuai WangEmail author
  • Li Li
  • Lianhua Chi
  • Ziping Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. An early diagnosis of neurodevelopmental disorders can improve treatment and significantly decrease associated healthcare cost, which reveals an urgent need for the development of ASD screening. However, the data used for ASD screening is heterogenous and multi-source, resulting in existing screening tools for ASD screening are expensive, time-intensive and sometimes fall short in predictive accuracy. In this paper, we apply novel feature engineering and feature encoding techniques, along with a deep learning classifier for ASD screening. Algorithms were created via a robust deep learning classifier and deep embedding representation for categorical variables to diagnose ASD based on behavioral features and individual characteristics. The proposed algorithm is effective compared with baselines, achieving 99% sensitivity and 99% specificity. The results suggest that deep embedding representation learning is a reliable method for ASD screening.

Keywords

Autism spectrum disorder Deep learning ASD screening Categorical embedding 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haishuai Wang
    • 1
    Email author
  • Li Li
    • 2
  • Lianhua Chi
    • 3
  • Ziping Zhao
    • 4
  1. 1.Fairfield UniversityFairfieldUSA
  2. 2.Harvard Medical SchoolBostonUSA
  3. 3.La Trobe UniversityMelbourneAustralia
  4. 4.Tianjin Normal UniversityTianjinChina

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