Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data.

ABSTRACT: Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algori

SUBMITTER: Taliaz D 

PROVIDER: S-EPMC8266902 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature