Depression Recognition

Depression recognition research aims to develop automated methods for identifying depression, improving early intervention and reducing the burden on mental health professionals. Current efforts focus on leveraging diverse data sources, including conversational transcripts, fMRI data, physiological signals (EEG, fNIRS, rPPG), and social media posts, employing machine learning models such as BERT, transformers, CNNs, RNNs, and ensemble methods like Random Forest-ANN combinations. These approaches aim to improve diagnostic accuracy and provide interpretable results, addressing challenges like data imbalance and the need for uncertainty quantification in clinical settings. The ultimate goal is to create reliable and accessible tools for early depression detection, facilitating timely treatment and improving patient outcomes.