Early detection of autism spectrum disorder in young children with machine learning using medical claims data

Objectives: Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data. Methods: Using the MarketScan Health Claims Database 2005-2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18-30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual's medical claims during early years postbirth as predictor variables. Results: For predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice. Conclusions: Our study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening.