Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross‐trial and cross‐drug replication

Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale MADRS).