TY - GEN
T1 - An evaluation of identification of suspected autism spectrum disorder (ASD) cases in early intervention (EI) records
AU - Liu, Mengwen
AU - An, Yuan
AU - Hu, Xiaohua
AU - Langer, Debra
AU - Newschaffer, Craig
AU - Shea, Lindsay
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84894524408&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894524408&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2013.6732559
DO - 10.1109/BIBM.2013.6732559
M3 - Conference contribution
AN - SCOPUS:84894524408
SN - 9781479913091
T3 - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
SP - 566
EP - 571
BT - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
T2 - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Y2 - 18 December 2013 through 21 December 2013
ER -