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

• Published in: European psychiatry 2020-01, Vol.63 (1), p.e22, Article e22
• Main Authors: Rahman, Rayees, Kodesh, Arad, Levine, Stephen Z, Sandin, Sven, Reichenberg, Abraham, Schlessinger, Avner
• Format: Article
• Language: English
• Subjects: Algorithms; autism spectrum disorder; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorder - epidemiology; Autistic Disorder - diagnosis; Child; Child, Preschool; electronic biomarker; Electronic Health Records - statistics & numerical data; Female; Humans; Infant, Newborn; Logistic Models; Machine Learning; Male; Medicin och hälsovetenskap; Parents; pharmacology; random forest; Risk Assessment; risk prediction
• ISSN: 0924-9338; 1778-3585; 1778-3585
• DOI: 10.1192/j.eurpsy.2020.17

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. 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 (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]). All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset. We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.