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Mental health disorders are a growing concern globally, and early diagnosis remains a challenge due to limited access to mental health professionals and inherent subjectivity in self-reporting methods. This paper introduces PsyPredict, an AI-based system for predicting mental health conditions such as depression and anxiety using multi-modal data inputs, including text, video-based emotion analysis, and real-time machine learning. Through the integration of these data sources, PsyPredict offers a comprehensive and accurate mental health assessment, providing timely, actionable interventions. This paper detail the methodology, implementation, and performance of the system, concluding with potential future applications and improvements.

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Multi-modal AI for Mental Health Prediction and Intervention

  • 会议论文

  • 发表日期：

    2026年

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  • Saphalya Das,
  • Mayukh Neogi,
  • Anasuya Sengupta

摘要

Mental health disorders are a growing concern globally, and early diagnosis remains a challenge due to limited access to mental health professionals and inherent subjectivity in self-reporting methods. This paper introduces PsyPredict, an AI-based system for predicting mental health conditions such as depression and anxiety using multi-modal data inputs, including text, video-based emotion analysis, and real-time machine learning. Through the integration of these data sources, PsyPredict offers a comprehensive and accurate mental health assessment, providing timely, actionable interventions. This paper detail the methodology, implementation, and performance of the system, concluding with potential future applications and improvements.