Daniel J. Müller | Nunez Lab for Computational Mental Health & Cancer Care

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report

Wed, 01 Nov 2023 00:00:00 +0000

This study demonstrates that machine learning can predict quality of life improvements in depression treatment, supporting the use of early clinical indicators to identify patients likely to benefit from antidepressant therapy in terms of functional outcomes.

Response Trajectories during Escitalopram Treatment of Patients with Major Depressive Disorder

Fri, 01 Sep 2023 00:00:00 +0000

This study reveals distinct response trajectories to escitalopram treatment in depression by applying unsupervised machine learning to clinical data. The findings highlight that subjective mood and anhedonia are central to treatment response, while other symptom domains show more variable patterns.

Replication of Machine Learning Methods to Predict Treatment Outcome with Antidepressant Medications in Patients with Major Depressive Disorder from STAR*D and CAN-BIND-1

Tue, 01 Jun 2021 00:00:00 +0000

This replication and external validation study demonstrates that machine learning methods can successfully predict antidepressant treatment outcomes, with prediction of remission performing better than prediction of response. These findings support the clinical utility of machine learning approaches for personalized depression treatment planning.