Quality of life is an important patient-centric outcome. This study used machine learning methods to predict which patients will experience quality of life improvement during antidepressant treatment for major depressive disorder. Using data from STAR*D and CAN-BIND-1 trials, the research found that quality of life improvement can be predicted using early depression symptoms and baseline quality of life impairment.
This study demonstrates that machine learning can predict quality of life improvements in depression treatment, supporting the use of early clinical indicators to identify patients likely to benefit from antidepressant therapy in terms of functional outcomes.