Depression Detection

Depression detection research aims to develop accurate and reliable methods for identifying depression using various data sources, primarily focusing on improving diagnostic accuracy and providing explainable results. Current research heavily utilizes machine learning, particularly deep learning models like transformers (e.g., BERT, GPT), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), often incorporating multimodal data (text, speech, images, video) and leveraging techniques like attention mechanisms and feature fusion. These advancements hold significant promise for improving early diagnosis and intervention, potentially leading to more effective and timely mental healthcare, particularly in resource-constrained settings.