Establishing Causality in Mental Health using AI

This talk explores the forefront of artificial intelligence (AI) in establishing causality in mental health. By leveraging Graph Neural Networks (GNNs) and Spatio-Temporal Graph Neural Networks (STGNNs), we aim to uncover causal relationships in complex mental health causal effects. The session will cover fundamental concepts of causality, the transition from traditional GNNs to STGNNs, and the creation of Clinical and User Knowledge Graphs (KGs). We will also delve into the novel GNN-RAG approach for enhancing reasoning in large language models and discuss the ethical, privacy, and regulatory challenges. Collaboration with healthcare professionals and stakeholders will be emphasized to ensure practical and ethical implementation.