Prediction of treatment outcome in major depressive disorder using ensemble of hybrid transfer learning and long short-term memory based on EEG signal

This paper presents a prediction model for treatment outcomes in major depressive disorder (MDD) using an ensemble of hybrid transfer learning and long short-term memory (LSTM) based on electroencephalogram (EEG) signals. The proposed model aims to improve the accuracy of predicting treatment outcomes for MDD patients. The study demonstrates the effectiveness of the model in analyzing EEG signals to predict treatment outcomes, offering a promising approach for personalized treatment strategies in MDD.

Keywords:

Major depressive disorder , Treatment outcome prediction , Ensemble learning , Transfer learning , Long short-term memory , EEG signal analysis