Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data
Spinrad, A., Darki-Morag, S. & Taliaz, D. Optimizing prediction of response to antidepressant medications using machine learning and environmental data. Eur. Psychiatry 64, S755–S755 (2021).
Current practice for Major depressive disorder (MDD) mainly relies on trial-and-error, with antidepressants (AD) estimated with 42%-53% response rates. Applying feature selection techniques and machine-learning algorithms on combinations of clinical and demographic factors may contribute to understanding MDD multifactorial complexity. Selected machine learning models for 5 antidepressants achieved a cumulative average accuracy of 60.12%.