# Depression Detection

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

Rafiq is an Arabic conversational system for early depression detection that combines per-message symptom monitoring, a three-phase PHQ-9 questionnaire delivered inside the chat, and retrieval-augmented answers grounded in a clinical knowledge base. On 415 held-out instances symptom detection reached 0.98 accuracy, while retrieval quality depended heavily on embedder choice: an Arabic-specific model scored 80% MAP and a general multilingual mixture-of-experts model only 4%.

Rafiq is an Arabic conversational system for early depression detection that combines per-message symptom monitoring, a three-phase PHQ-9 questionnaire delivered inside the chat, and retrieval-augmented answers grounded in a clinical knowledge base. On 415 held-out instances symptom detection reached 0.98 accuracy, while retrieval quality depended heavily on embedder choice: an Arabic-specific model scored 80% MAP and a general multilingual mixture-of-experts model only 4%.