Depression Risk

Research on depression risk prediction focuses on developing accurate and interpretable methods for early identification, aiming to improve diagnosis and treatment outcomes. Current approaches utilize diverse data sources, including street-view imagery, census data, audio recordings, gait analysis, social media posts, and fMRI scans, employing machine learning algorithms like deep neural networks (e.g., ResNet, DeiT), random effects models (RE-EM, MERF), and ensemble methods to analyze these complex datasets. These advancements hold significant promise for improving the efficiency and accuracy of depression screening, particularly in primary care and telehealth settings, and for identifying individuals at high risk before symptoms become severe. However, ongoing work addresses crucial issues of bias and fairness in these models to ensure equitable application across diverse populations.