{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["3(6)"],"submitter":["Rajpurkar P"],"pubmed_abstract":["<h4>Importance</h4>Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.<h4>Objective</h4>To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.<h4>Design, setting, and participants</h4>This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant m"],"journal":["JAMA network open"],"pagination":["e206653"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7309440"],"repository":["biostudies-literature"],"pubmed_title":["Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial."],"pmcid":["PMC7309440"],"pubmed_authors":["Vale V","Yang J","Taylor Z","Irvin J","Keller AS","Basu S","Rajpurkar P","Dass N","Ng A","Williams LM"],"additional_accession":[]},"is_claimable":false,"name":"Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.","description":"<h4>Importance</h4>Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.<h4>Objective</h4>To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.<h4>Design, setting, and participants</h4>This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant m","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jun","modification":"2026-05-04T08:23:52.845Z","creation":"2020-06-28T07:05:09Z"},"accession":"S-EPMC7309440","cross_references":{"pubmed":["32568399"],"doi":["10.1001/jamanetworkopen.2020.6653"]}}