An evaluation of identification of suspected autism spectrum disorder (ASD) cases in early intervention (EI) records

Mengwen Liu, Yuan An, Xiaohua Hu, Debra Langer, Craig Newschaffer, Lindsay Shea

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

6 Scopus citations

Abstract

The rising prevalence of Autism Spectrum Disorder (ASD) in the United States points to an increased need for services across the life span. Specialized services beginning at the earliest age possible are critical to maximizing long-term outcomes for children with ASD and their families. Many children later diagnosed with ASD will begin to receive services through the federally funded Early Intervention (EI) system that serves infants and toddlers from birth to age three. However, without formal recognition, services may not fully address the constellation of ASD symptoms. While ASD training in EI is becoming more widespread, there is still a need for better detection of ASD symptoms at younger ages. We hypothesized that initial EI assessment records which document the strengths and needs of children in EI, could be an important source for detecting ASD warning signs and aid state EI systems in earlier identification. In this research, we used EI records to evaluate classification techniques to identify suspected ASD cases. We improved the performance of machine learning techniques by developing and applying a unified ASD ontology to identify the most relevant features from EI records. The results indicate that using Support Vector Machine (SVM) with ontology-based unigrams as features yields the best performance. Our study shows that developing automatic approaches for quickly and effectively detecting suspected cases of ASD from non-standardized EI records earlier than most ASD cases are typically detected is promising.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Pages566-571
Number of pages6
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China
Duration: Dec 18 2013Dec 21 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

Other

Other2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
CountryChina
CityShanghai
Period12/18/1312/21/13

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All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

Liu, M., An, Y., Hu, X., Langer, D., Newschaffer, C., & Shea, L. (2013). An evaluation of identification of suspected autism spectrum disorder (ASD) cases in early intervention (EI) records. In Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 (pp. 566-571). [6732559] (Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013). https://doi.org/10.1109/BIBM.2013.6732559