Differential Treatment Benefit Prediction For Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

Background: Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability. Methods: Using STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature selection. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naive approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population. Results: Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% improvement in population remission rate (p= 0.01, C.I. 2.48% +/- .5%). Conclusion: Our model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from a number of treatment options. These models may have significant real-world clinical implications.