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PSI Conference 2018: A machine learning approach for clinical subtyping in autism spectrum disorder

06 June 2018

In a subtyping approach, the supposedly continuous manifestation of Autism Spectrum Disorder (ASD) will be decomposed using clusters which are defined on the basis of clinical heterogeneity in subdomains covering e.g. repetitive behaviors, social interaction, cognition or communication ability.  Approximately 2800  patients from the Simons Simplex Collection who have been diagnosed with ASD comprising subtypes of e.g. Idiopathic Autism or Aspergers and have valid scores on the ADI-R, SRS, RBS-R, and Vineland-II scales.  To restrict the dimension of the dataspace, predominantly standardized composite scores will be used in  lieu of item-level scores. Patients will be subjected to clustering applying Self-Organizing Maps (SOM) which is a well-established machine learning technique. Findings of SOM patient clustering will be discussed, and compared to age and IQ patterns. It will be shown that SOM is able to embed the term ‘correlation’ into a broader i.e. non-linear context.

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