Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence.

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 excludi

SUBMITTER: Nemesure MD 

PROVIDER: S-EPMC7820000 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature