Optimizing precision medicine for second-step depression treatment: a machine learning approach.
Journal:
Psychological medicine
Published Date:
Mar 27, 2024
Abstract
BACKGROUND: Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The process for determining appropriate second-step care is often based on clinical intuition and involves a protracted course of trial and error, resulting in substantial patient burden and unnecessary delay in the provision of optimal treatment. To address this problem, we adopt an ensemble machine learning approach to improve prediction accuracy of remission in response to second-step treatments.