Treatment Response | Nunez Lab for Computational Mental Health & Cancer Carehttps://example.com/tag/treatment-response/Treatment ResponseWowchemy (https://wowchemy.com)en-usFri, 01 Sep 2023 00:00:00 +0000https://example.com/media/icon_huf7bff94e036df031df1d4c9565d99cb9_25138_512x512_fill_lanczos_center_3.pngTreatment Responsehttps://example.com/tag/treatment-response/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>Replication of Machine Learning Methods to Predict Treatment Outcome with Antidepressant Medications in Patients with Major Depressive Disorder from STAR*D and CAN-BIND-1https://example.com/publication/antidepressant_ml_replication/Tue, 01 Jun 2021 00:00:00 +0000https://example.com/publication/antidepressant_ml_replication/<p>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.</p>