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

ABSTRACT:

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. Routi

SUBMITTER: Rahman R 

PROVIDER: S-EPMC7315872 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature