Major Depressive Disorder

Major Depressive Disorder (MDD) research focuses on improving diagnosis and treatment personalization, driven by the need for more effective and accessible interventions. Current efforts utilize machine learning, employing diverse model architectures like convolutional neural networks, recurrent neural networks (including transformers and LSTMs), and Bayesian networks, to analyze various data modalities including speech, facial expressions, text from clinical interviews, and neuroimaging data (EEG and fMRI). These analyses aim to identify robust biomarkers for diagnosis, predict treatment response, and ultimately enhance the precision and efficacy of MDD care. The ultimate goal is to develop more accurate diagnostic tools and personalized treatment strategies, leading to improved patient outcomes.