Identification of newborns at risk for autism using electronic medical records and machine learning

AbstractBackground.Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis...

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Auteurs principaux:	Rayees Rahman, Arad Kodesh, Stephen Z. Levine, Sven Sandin, Abraham Reichenberg, Avner Schlessinger
Format:	Artigo
Langue:	Inglês
Publié:	Cambridge University Press 2020-01-01
Collection:	European Psychiatry
Sujets:	autism spectrum disorder electronic biomarker random forest pharmacology risk prediction
Accès en ligne:	https://www.cambridge.org/core/product/identifier/S0924933820000176/type/journal_article
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