Optimizing prediction of response to antidepressant medications using machine learning and environmental 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).

Summary

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%.

 

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