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).

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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