Current methods of choosing treatment for major depressive disorder (MDD) are inefficient.
The Strategic Treatment to Achieve Remission of Depression (STAR*D) Trial revealed that only
about 1/3 of patients treated with antidepressant drugs will go into remission with the first
medication chosen. We hypothesize that pattern recognition software using Machine Learning
methods can accurately predict response to a variety of antidepressant medications (ADM) or
cognitive behavior therapy (CBT) after training using pre-treatment demographic, clinical,
laboratory or electroencephalographic (EEG) data. These algorithms might assist the clinician
to chose, for any given patient, an antidepressant treatment option with greater probability
of favourable response than is achievable using current best practise methods.