TY - JOUR
AU1 - Rajpurkar, Pranav
AU2 - Yang, Jingbo
AU3 - Dass, Nathan
AU4 - Vale, Vinjai
AU5 - Keller, Arielle S.
AU6 - Irvin, Jeremy
AU7 - Taylor, Zachary
AU8 - Basu, Sanjay
AU9 - Ng, Andrew
AU1 - Williams, Leanne M.
AB - Key PointsQuestionCan machine learning models predict improvement of various depressive symptoms with antidepressant treatment based on pretreatment symptom scores and electroencephalographic measures?FindingsIn this prognostic study, using the machine learning approach of gradient-boosted decision trees, the ElecTreeScore algorithm could reliably distinguish the patients who responded to treatment from those who did not based on various depressive symptoms using pretreatment symptom scores and electroencephalographic features (using the cross-validation approach on 518 patients).MeaningMachine learning approaches that include pretreatment symptom scores and electroencephalographic features may help predict which depressive symptoms will improve with antidepressants.
TI - Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression
JF - JAMA Network Open
DO - 10.1001/jamanetworkopen.2020.6653
DA - 2020-06-22
UR - https://www.deepdyve.com/lp/pubmed-central/evaluation-of-a-machine-learning-model-based-on-pretreatment-symptoms-65gze1oudk
SP - e206653
VL - 3
IS - 6
DP - DeepDyve
ER -