Predicting post-treatment symptom trajectories is crucial in order to inform decisions concerning type, intensity, and duration of treatment. A large body of research shows associations between predictors and post-treatment outcomes in samples with alcohol use disorder (AUD), but these models do not provide adequate predictions for an individual patient. Recently, machine learning algorithms have been used to establish predictive models in substance use disorder research. MLAUD aims to expand this research and to investigate how machine learning algorithms can be used to improve individual, post-treatment outcome predictions for patients with AUD.
«MLAUD on research gate»: https://www.researchgate.net/project/A-Machine-Learning-Based-Approach-to-Predict-Post-Treatment-Drinking-Behavior-in-Patients-with-Alcohol-Use-Disorder