This research explores using multimodal machine learning to create more objective, accessible mental health diagnostic tools for conditions like depression and PTSD.

• Combines and analyzes text, audio, and video data to capture complementary information
• Employs advanced embedding techniques to process different data modalities
• Addresses critical limitations in traditional clinical assessments including subjectivity and accessibility
• Aims to develop scalable tools for more consistent mental health evaluation

This innovation has significant potential to transform mental healthcare by enabling earlier, more accurate diagnoses and expanding access to assessment tools in underserved communities.

Leveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment