Abstract
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.
Highlights
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays
GAD represents a persistent, uncontrollable pattern of worry occurring in multiple domains of an individual’s life[7]. These syndromes often have devastating consequences for affected individuals, their families, and communities[8,9]. Both MDD and GAD are prevalent in the college population
There is an estimated 6 year and 14 year delay between disease onset and intervention for MDD and GAD, respectively, during which time the disease may increase in severity, lowering student quality of life[17,18]
Summary
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are prevalent psychiatric disorders that affect 16.2% and 13.3% of U.S individuals, respectively, over their lifetimes[1,2]. GAD represents a persistent, uncontrollable pattern of worry occurring in multiple domains of an individual’s life[7] Left untreated, these syndromes often have devastating consequences for affected individuals, their families, and communities[8,9]. A 2019 study showed a 20% prevalence of GAD among college students in 2016, representing a 100% increase since 2 00811 These syndromes negatively impact multiple domains of an individual’s functioning, and for college students, this may include interference with. There is an estimated 6 year and 14 year delay between disease onset and intervention for MDD and GAD, respectively, during which time the disease may increase in severity, lowering student quality of life[17,18]
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