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Artificial intelligence has a lot to offer in the crucial field of mental health diagnoses. Our research suggests a unique method for identifying the subtle emotional and cognitive patterns from voice, text, and physiological data using multimodal neural networks. The system's goal is to offer early identification of mental health conditions including post-traumatic stress disorder (PTSD), anxiety and depression. The suggested solution outperforms unimodal methods in terms of diagnosis accuracy by utilizing deep learning models that are customized for every data modality and combining their results. The outline of the design, methods, and findings of the experiments showcasing this intelligent system's potential as a diagnostic aid for mental health practitioners.

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Multimodal Emotion-Aware Neural Networks for Early Diagnosis of Mental Health Disorders

  • 会议论文

  • 发表日期：

    2026年

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  • R. S. Pavithra,
  • Mandhula Trupthi

摘要

Artificial intelligence has a lot to offer in the crucial field of mental health diagnoses. Our research suggests a unique method for identifying the subtle emotional and cognitive patterns from voice, text, and physiological data using multimodal neural networks. The system's goal is to offer early identification of mental health conditions including post-traumatic stress disorder (PTSD), anxiety and depression. The suggested solution outperforms unimodal methods in terms of diagnosis accuracy by utilizing deep learning models that are customized for every data modality and combining their results. The outline of the design, methods, and findings of the experiments showcasing this intelligent system's potential as a diagnostic aid for mental health practitioners.