Evaluation of a machine learning model based on pretreatment symptoms and electroencephalographic features to predict outcomes of antidepressant treatment in …

P Rajpurkar, J Yang, N Dass, V Vale… - JAMA network …, 2020 - jamanetwork.com
Importance Despite the high prevalence and potential outcomes of major depressive
disorder, whether and how patients will respond to antidepressant medications is not easily …
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[HTML][HTML] Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant …

P Rajpurkar, J Yang, N Dass, V Vale, AS Keller… - JAMA Network …, 2020 - ncbi.nlm.nih.gov
Objective To identify the extent to which a machine learning approach, using gradient-
boosted decision trees, can predict acute improvement for individual depressive symptoms …

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment …

P Rajpurkar, J Yang, N Dass, V Vale… - JAMA Network …, 2020 - search.ebscohost.com
Abstract Key Points: Question: Can machine learning models predict improvement of various
depressive symptoms with antidepressant treatment based on pretreatment symptom scores …

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment …

P Rajpurkar, J Yang, N Dass, V Vale, AS Keller, J Irvin… - 2020 - pubmed.ncbi.nlm.nih.gov
Importance Despite the high prevalence and potential outcomes of major depressive
disorder, whether and how patients will respond to antidepressant medications is not easily …

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment …

P Rajpurkar, J Yang, N Dass, V Vale, AS Keller… - JAMA Network …, 2020 - europepmc.org
Objective To identify the extent to which a machine learning approach, using gradient-
boosted decision trees, can predict acute improvement for individual depressive symptoms …

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment …

P Rajpurkar, J Yang, N Dass, V Vale, AS Keller… - JAMA Network …, 2020 - europepmc.org
Objective To identify the extent to which a machine learning approach, using gradient-
boosted decision trees, can predict acute improvement for individual depressive symptoms …