Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.

ABSTRACT: To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white bloo

SUBMITTER: Caly H 

PROVIDER: S-EPMC7994821 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature