AAI_2025_Capstone_Chronicles_Combined

MENTAL HEALTH RISK DETECTION USING ML​

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validated inputs or continuous data streams could further improve generalization and support dynamic risk assessment. To productionize this work, we will prioritize HIPAA compliance, user consent protocols, and the development of clinician-facing dashboards to facilitate transparent and actionable insights. To demonstrate the model, we created a user-friendly Gradio-based interface that showcases potential usability. This prototype could be expanded into a full-featured interface tailored for clinical or institutional use, with visualizations and risk breakdowns designed to support professional decision-making. While results are promising, real-world testing is essential to evaluate performance in live settings. Key ethical considerations include privacy, consent, algorithmic bias, and fairness before broader deployment in schools, workplaces, or healthcare systems.

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