Mental Disorder Classification

Mental disorder classification aims to automatically identify mental health conditions using various data sources, including social media text, clinical notes, and neuroimaging data. Current research focuses on developing sophisticated machine learning models, such as multimodal transformers and those leveraging temporal information in text sequences, to improve diagnostic accuracy and efficiency. These advancements hold significant promise for augmenting clinical practice, enabling earlier intervention, and potentially improving the accessibility and scalability of mental healthcare. The field is also actively addressing challenges related to data scarcity, ethical considerations, and the need for robust and generalizable models across diverse populations and disorders.