  INTRODUCTION: The pharmacological treatment of Major Depressive Disorder
(MDD) relies on a trial-and-error approach. We introduce an artificial
intelligence (AI) model aiming to personalize treatment and improve outcomes,
which was deployed in the Artificial Intelligence in Depression Medication
Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting
probabilities of remission across multiple pharmacological treatments for
adults with at least moderate major depression. 2) Validate model predictions
and examine them for amplification of harmful biases. METHODS: Data from
previous clinical trials of antidepressant medications were standardized into a
common framework and included 9,042 adults with moderate to severe major
depression. Feature selection retained 25 clinical and demographic variables.
Using Bayesian optimization, a deep learning model was trained on the training
set, refined using the validation set, and tested once on the held-out test
set. RESULTS: In the evaluation on the held-out test set, the model
demonstrated achieved an AUC of 0.65. The model outperformed a null model on
the test set (p = 0.01). The model demonstrated clinical utility, achieving an
absolute improvement in population remission rate in hypothetical and actual
improvement testing. While the model did identify one drug (escitalopram) as
generally outperforming the other drugs (consistent with the input data), there
was otherwise significant variation in drug rankings. On bias testing, the
model did not amplify potentially harmful biases. CONCLUSIONS: We demonstrate
the first model capable of predicting outcomes for 10 different treatment
options for patients with MDD, intended to be used at or near the start of
treatment to personalize treatment. The model was put into clinical practice
during the AIDME randomized controlled trial whose results are reported
separately.
