A novel ML algorithm (named ‘AutMedAI’) capable of early autism spectrum disorder (ASD) detection with minimal background and medical information.
By Hugo Francisco de SouzaReviewed by Lily Ramsey, LLMAug 22 2024 In a recent study published in JAMA Network Open, researchers developed and validated four machine learning models on a dataset comprising more than 30,000 participants to identify a novel ML algorithm capable of early autism spectrum disorder detection with minimal background and medical information.
This study represents a promising first step in the ideal of early and routine ASD detection, saving patients and their families substantial socioeconomic stress and improving their future quality of life. While the psychological mechanisms underpinning ASD are multifaceted and beyond the scope of the present work, early detection and subsequent interventions are the best forms of recourse for improved population-wide outcomes.
Unfortunately, these models have only been validated in research settings and appear to require expansive raw data for robust predictions. Study data included: 1. Primary medical screening information, 2. Background family history, 3. Demographic information, including patients’ race/ethnicity, 4. Questionnaire scores, specifically the Child Behavior Checklist , the Social Communication Questionnaire score, and the Full-scale Intelligence Quotient . Of these, 28 variables were chosen for their ease of acquisition and applicability to patients under 24 months.
Study findings The training and validation cohort comprised 30,660 participants . Participants were found to be 63.5% male and predominantly White with a mean age of 113 months.
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