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Identification of Newborns at Risk for Autism Using Electronic Medical Records and Machine Learning by Rayees Rahman; Arad Kodesh; Stephen Z. Levine; Sven Sandin; Abraham Reichenberg; Avner Schlessinger is a Psychology article available to read on EtoBox.

What is Identification of Newborns at Risk for Autism Using Electronic Medical Records and Machine Learning about?

## Background. 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 and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample. ## Methods. We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve

Who reads Identification of Newborns at Risk for Autism Using Electronic Medical Records and Machine Learning?

Author

Rayees Rahman; Arad Kodesh; Stephen Z. Levine; Sven Sandin; Abraham Reichenberg; Avner Schlessinger

Publisher

Elsevier Science; Elsevier ; Elsevier Masson; Cambridge University Press (CUP); Elsevier BV; Publons; Royal College of Psychiatrists (ISSN 0924-9338)

Published

2020

Language

EN

Field

Psychology (Social Sciences)