Depression Forecasting

Depression forecasting research aims to predict the onset, severity, or progression of depression using various data sources and machine learning models. Current efforts focus on leveraging diverse data, including medical imaging (e.g., MRI) analyzed with quantum-enhanced and classical neural networks (like LSTM and convolutional architectures), and passively collected mobile phone sensor data processed with LSTM networks. These approaches aim to improve early intervention and personalized treatment strategies by identifying individuals at high risk and predicting future depressive episodes, offering a potential shift from reactive to proactive mental healthcare.