Imputing cognitive impairment in SPARK, a large autism cohort

Imputing cognitive impairment in SPARK, a large autism cohort

Diverse large cohorts are necessary for dissecting subtypes of autism, and intellectual disability is one of the most robust endophenotypes for analysis. However, current cognitive assessment methods are not feasible at scale. We developed five commonly used machine learning models to predict cognit...

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Veröffentlicht in:Autism research 2022-01, Vol.15 (1), p.156-170
Hauptverfasser: Shu, Chang, Green Snyder, LeeAnne, Shen, Yufeng, Chung, Wendy K.
Format: Artikel
Sprache:eng
Schlagworte:
Autism
Autism Spectrum Disorder
Autistic children
Autistic Disorder
Child
Children
Cognition
Cognitive ability
Cognitive Dysfunction - complications
cognitive impairment
Humans
Impairment
imputation
Intellectual disabilities
Intelligence
intelligence quotient
Learning algorithms
Machine learning
Missing data
Parents
Sensitivity analysis
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Zusammenfassung:Diverse large cohorts are necessary for dissecting subtypes of autism, and intellectual disability is one of the most robust endophenotypes for analysis. However, current cognitive assessment methods are not feasible at scale. We developed five commonly used machine learning models to predict cognitive impairment (FSIQ
ISSN:1939-3792
1939-3806
DOI:10.1002/aur.2622