Comparison of area under the receiver operating characteristic curve with combined versus separated inpatient and outpatient encounters by LASSO logistic regression and random forest models, at the age of 18, 24 and 30 months, respectively. Error bars in the figure represent the 95% CIs based on results from 50 replications of independent runs. LASSO, least absolute shrinkage and selection operator.
In our study, both LASSO LR and RF models showed promising accuracy in predicting ASD diagnosis based on an individual’s medical claims data. This robust finding implies that there may exist distinct patterns in health conditions and health service needs among young children with ASD, well before the onset of most hallmark ASD behavioural symptoms.
Our study has made an important contribution to applying health informatics in the field of ASD. Although there exists a plethora of literature identifying individual risk factors of ASD, using large healthcare service data and machine learning models to systematically predict ASD diagnosis has remained much less explored.
Our study has several limitations. First, diagnosis of ASD established only based on existing diagnosis codes from claims data could be inaccurate and unreliable sometimes in practice. We followed a validated approach in ASD health service research literature to identify the ASD cohort in our study.
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