Deep learning based multimedia data mining for autism spectrum disorder (ASD) diagnosis

Saad Sadiq, Micheal Castellanos, Jacquelyn Moffitt, Mei Ling Shyu, Lynn Perry, Daniel Messinger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by deficits in social communication and restricted and repetitive patterns of behavior. Autism is estimated to affect 1 in 59 children in the United States and costs roughly 35B to the society. Early diagnosis of ASD is vital for promoting early intervention and positive developmental outcomes. Traditional diagnostic procedures for ASD include structured behavioral observation by a trained clinician. Diagnosticians typically rely on the Autism Diagnostic Observation Schedule (ADOS-2) to quantify ASD symptoms. In this paper, we take a parallel approach and investigate language modalities and discover associations between objective measurements of social communication and ASD symptoms. We analyze 33 children with autism and extract their linguistic patterns from their conversations with diagnosticians in a clinical setting. Our methods use Long-Short Term Memory (LSTM) networks to learn Speech Activity Detection (SAD) and speaker diarization patterns to generate the vocal turn-taking metrics. We then use our novel proposed pipeline to predict the ADOS-2 Calibrated Severity Scores (CSS) of Social Affect (SA). The proposed framework achieve state-of-the-art predictive diagnostic estimates of ASD severity compared to industry's leading algorithms. Results compared with the language acquisition system Language ENvironment Analysis (LENA) and other algorithms indicate a significant improvement in the R2 measure.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
PublisherIEEE Computer Society
Pages847-854
Number of pages8
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2019-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
CountryChina
CityBeijing
Period11/8/1911/11/19

Keywords

  • Autism spectrum disorder (ASD)
  • Deep learning
  • Medical Diagnostics
  • Multimedia Data mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Fingerprint Dive into the research topics of 'Deep learning based multimedia data mining for autism spectrum disorder (ASD) diagnosis'. Together they form a unique fingerprint.

Cite this