2016 Gatlinburg Conference Poster
PS-32
Title: Using Machine Learning to Differentiate Autism Spectrum Disorder Risk Status Groups on M-CHAT-R Responses
Authors: Reina S. Factor, Luke E. K. Achenie, Angela Scarpa, Marlene V. Strege, Diana L. Robins, Scott McCrickard
Introduction: Early screening of Autism Spectrum Disorder (ASD) improves prognosis by facilitating early intervention. The
Modified Checklist for Autism in Toddlers (M-CHAT; Robins, Fein, & Barton, 1999) is an evidence-based gold standard parentreport instrument recommended by the American Academy of Pediatrics for screening in primary care settings (Johnson &
Myers, 2007). The M-CHAT-Revised (M-CHAT-R; Robins, Fein, & Barton, 2009) includes 20 items (Robins et al., 2014).
A recent survey found only 60% of pediatricians reported using formal screening for ASD at 18 months and only 50% at 24
months (Arunyanart et al., 2012). Since current screening practices rely on clinician training and subjective judgment, machine
learning (ML) may serve as a powerful complementary tool. ML techniques represent a class of artificial intelligence, capable of
using large databases to learn patterns consistent with ASD traits. The M-CHAT-R must be hand-scored, interpreted, and
followed up with an interview. In contrast, the ML tool is automatically scored, makes objective decisions with minimal human
bias, and requires no clinical training. Self-Organizing Maps (SOM) is a specific kind of ML that clusters data according to similar
responses (Bock, 2003).
Methods: The current study examined the use of ML to determine ASD risk status by using fewer M-CHAT-R items. Archival data
consisted of 16,177 toddlers (16-30 months, collected during their 18- or 24- month well visit). Toddlers missing M-CHAT-R
responses were excluded and the sample used included 14,995 toddlers (46.51% male), with 15 years average maternal
education (Range=11-20 years). Children at risk were referred for diagnostic assessment. MATLAB script was created to interface
with the SOM Toolbox (SOM Toolbox Team, Helsinki University of Technology, Finland) and was run for different data sets. All 20
M-CHAT-R questions were included as inputs and SOM selected the most informative questions and grouped data into clusters
based on similarity of traits. Item analysis indicated key risk status group differences.
Results: SOM was able to separate toddlers into risk status groups, based on fewer items (12/20), and identify key symptom
presentation differences. Three risk status groups were identified: Typically Developing (TD), Low Risk, and High Risk. Risk status
indicates that a toddler may be at risk for any developmental disability, including ASD. As such, TD participants showed no more
than one flagged item, Low Risk participants demonstrated more than one ASD-like trait, and High Risk participants
demonstrated a number of ASD-like traits. All confirmed ASD cases fell in the High risk group.
Discussion: Overall, findings support the hypothesis that SOM was able to differentiate individuals based on risk status and do
so using fewer items. This has implications for early detection. These results are consistent with the most recent M-CHAT-R
findings, indicating that although screening positive does not necessarily indicate an ASD diagnosis, it is a good predicator of
developmental delay or concern. Specifically, these results predicted 100% of screen positive toddlers, higher than the rates
determined by previous clinical scoring (Robins et al., 2014). Therefore, groups highlighted by SOMtool might be a more accurate
reflection of risk status. Applying SOM in a mobile application could make the MCHAT-R easier to use in pediatric settings, while
maintaining accurate results.
References/Citations:
• Arunyanart, W., Fenick, A., Ukritchon, S., Imjaijitt, W., Northrup, V., Weitzman, C., Developmental and Autism Screening:
A Survey across Six States. Infants and Young Children, 2012. 25(3): p. 175-187.
• Bock, T. A new approach for exploring multivariate data: self-organizing maps. International. Journal of Market
Research, 2004. 46(2):189-203
• Robins, D.L., Casagrande, K., Barton, M., Chen, C.A., Dumont-Mathieu, T., Fein, D. , Validation of the Modified Checklist
for Autism in Toddlers, Revised With Follow-up (M-CHAT-R/F). Pediatrics, 2014. 133(1): p. 37-45.