From machine learning to deep learning: A comprehensive study of alcohol and drug use disorder

This study aims to train and validate machine learning deep models identify patients with risky alcohol drug misuse in a Screening, Brief Intervention, Referral Treatment (SBIRT) program. An observational cohort of 6978 adults was admitted the western region Alabama at three medical facilities between January December 2019. Data were cleaned pre-processed using data imputation techniques an augmented sampling method. The primary analysis involved multi-class classification misuse. Our shows that accurate identification use screening instrument scores best accomplished mixed-effects following missing Generative Adversarial Imputation Networks (GAIN) method then followed by applying Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) augmentation Although mixed are commonly employed studies electronic health records (EHRs), GAIN SMOTE-NC for diagnosing disorder is novel original.