Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder.

ABSTRACT:

Background

Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males.

Methods

Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % fema

SUBMITTER: Stroth S 

PROVIDER: S-EPMC8900413 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature