Valerie H. Taylor | Nunez Lab for Computational Mental Health & Cancer Carehttps://example.com/authors/valerie-h.-taylor/Valerie H. TaylorWowchemy (https://wowchemy.com)en-usWed, 01 Nov 2023 00:00:00 +0000https://example.com/media/icon_huf7bff94e036df031df1d4c9565d99cb9_25138_512x512_fill_lanczos_center_3.pngValerie H. Taylorhttps://example.com/authors/valerie-h.-taylor/Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Reporthttps://example.com/publication/quality_life_antidepressant_ml/Wed, 01 Nov 2023 00:00:00 +0000https://example.com/publication/quality_life_antidepressant_ml/<p>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.</p>Response Trajectories during Escitalopram Treatment of Patients with Major Depressive Disorderhttps://example.com/publication/escitalopram_response_trajectories/Fri, 01 Sep 2023 00:00:00 +0000https://example.com/publication/escitalopram_response_trajectories/<p>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.</p>