Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression – Medication Enhancement (AID-ME) Study

Abstract

We introduce an artificial intelligence (AI) model aiming to personalize treatment in adult major depression, which was deployed in the Artificial Intelligence in Depression: Medication Enhancement (AID-ME) Study. Our objectives were to predict probabilities of remission across multiple pharmacological treatments, validate model predictions, and examine them for biases. Data from 9,042 adults with moderate to severe major depression from antidepressant clinical trials were standardized into a common framework and feature selection retained 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set and refined using the validation set. On the held-out test set, the model demonstrated an AUC of 0.65 and outperformed a null model (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 identified escitalopram as generally outperforming other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. The model did not amplify potentially harmful biases. We demonstrate the first model capable of predicting outcomes for 10 treatments, intended to be used at or near the start of treatment to personalize treatment; AID-ME cluster randomized trial results are reported separately.