Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression

Abstract

Key Points Question Can machine learning models predict improvement of various depressive symptoms with antidepressant treatment based on pretreatment symptom scores and electroencephalographic measures? Findings In 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). Meaning Machine learning approaches that include pretreatment symptom scores and electroencephalographic features may help predict which depressive symptoms will improve with antidepressants.