The project aims to address the challenge of identifying individuals who may be at risk of suicide or experiencing deteriorating mental health. Traditional methods of mental health assessment often rely on subjective evaluations and self-reporting, making it difficult to intervene effectively. By leveraging Artificial Intelligence (AI) and Machine Learning (ML), the system can analyze various data sources, including EEG datasets, to detect specific disorders and provide personalized assessments and interventions. This proactive approach enables timely intervention and support, potentially saving lives and improving mental health outcomes.
Data Complexity: EEG datasets are complex and require careful preprocessing to ensure quality. Access to diverse and representative datasets can be limited.
Model Interpretability: AI models must be interpretable to build trust in the medical community. Understanding how decisions are made is crucial.
Ethical Considerations: Privacy, consent, and potential biases in data or algorithms must be addressed responsibly.
Healthcare Integration: Integrating AI solutions into existing healthcare systems, including EHR compatibility and regulatory compliance, can be challenging.
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