Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) with a high rate of comorbidity. The implementation of eye-tracking methodologies has informed behavioral and neurophysiological patterns of visual processing across ASD and comorbid NDDs. In this study, we propose a machine learning method to predict measures of two core ASD characteristics: impaired social interactions and communication, and restricted, repetitive, and stereotyped behaviors and interests. Our method extracts behavioral features from task performance and eye-tracking data collected during a facial emotion recognition paradigm. We achieved high regression accuracy using a Random Forest regressor trained to predict scores on the SRS-2 and RBS-R assessments; this approach may serve as a classifier for ASD diagnosis.
|Original language||English (US)|
|Title of host publication||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society|
|Subtitle of host publication||Enabling Innovative Technologies for Global Healthcare, EMBC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Jul 2020|
|Event||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada|
Duration: Jul 20 2020 → Jul 24 2020
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020|
|Period||7/20/20 → 7/24/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
PubMed: MeSH publication types
- Journal Article