  Major depressive disorder (MDD) is a heterogeneous condition; multiple
underlying neurobiological substrates could be associated with treatment
response variability. Understanding the sources of this variability and
predicting outcomes has been elusive. Machine learning has shown promise in
predicting treatment response in MDD, but one limitation has been the lack of
clinical interpretability of machine learning models. We analyzed data from six
clinical trials of pharmacological treatment for depression (total n = 5438)
using the Differential Prototypes Neural Network (DPNN), a neural network model
that derives patient prototypes which can be used to derive treatment-relevant
patient clusters while learning to generate probabilities for differential
treatment response. A model classifying remission and outputting individual
remission probabilities for five first-line monotherapies and three combination
treatments was trained using clinical and demographic data. Model validity and
clinical utility were measured based on area under the curve (AUC) and expected
improvement in sample remission rate with model-guided treatment, respectively.
Post-hoc analyses yielded clusters (subgroups) based on patient prototypes
learned during training. Prototypes were evaluated for interpretability by
assessing differences in feature distributions and treatment-specific outcomes.
A 3-prototype model achieved an AUC of 0.66 and an expected absolute
improvement in population remission rate compared to the sample remission rate.
We identified three treatment-relevant patient clusters which were clinically
interpretable. It is possible to produce novel treatment-relevant patient
profiles using machine learning models; doing so may improve precision medicine
for depression. Note: This model is not currently the subject of any active
clinical trials and is not intended for clinical use.
