The Fifth Elephant 2024 Annual Conference (12th &13th July)

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.

Outline

Introduction to Causality in Mental Health

• Importance of causality in understanding mental health.
  -- Traditional vs. AI-driven approaches.
  -- Fundamentals of Causality

• Overview of causality concepts.
  -- Methods for causal inference: correlation vs. causation.
  -- Graph Neural Networks (GNNs) in Causal Inference

• Introduction to GNNs and their applications.
  -- How GNNs help in causal discovery and inference.
  -- Case studies in mental health using GNNs.
  -- Time + Directionality = Causality

• The role of temporal and directional data in establishing causality.
  -- Techniques to model time and directionality in mental health data.
  -- From GNNs to Spatio-Temporal Graph Neural Networks (STGNNs)

• Evolution from GNNs to STGNNs.
  -- How STGNNs enhance causal analysis by incorporating spatial and temporal dimensions.
  -- Applications in mental health scenarios.
  -- Creating Clinical and User Knowledge Graphs (KGs)

• Building and utilizing KGs to represent clinical and user data.
  -- Integration of KGs with GNNs and STGNNs for richer causal insights.
  -- GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

• Overview of GNN-RAG methodology.
  -- Enhancing reasoning capabilities of large language models using GNN-RAG.
  -- Implications for mental health analysis.
  -- Privacy, Ethics, and Regulatory Considerations

• Key privacy concerns with AI in mental health.
  -- Ethical issues and the need for transparent AI models.
  -- Navigating regulatory landscapes and ensuring compliance.
  -- Collaboration with Healthcare Professionals & Other Stakeholders

• The importance of interdisciplinary collaboration.
  -- Strategies for effective communication and implementation of AI in clinical settings.