Role of Knowledge Graphs in Mental Health Diagnosis and Cure
summary
Links food and biochemical mentions from corpora to mental illness using knowledge graphs and claims that Neo4j-backed GCNs can predict biomarkers and classify disorders, with demonstrations of node embeddings, nearest-neighbor retrieval, and recommendations built on Neo4j + Vertex AI. Pipeline deploys Neo4j on GCP, uses LangChain prompts, Cipher text-to-SQL, RAG grounding into vector DBs, and a Gradio chatbot; shows code snippets and working examples. Cites 57.8M US adults affected and a $6T global cost projection by 2030, and enforces privacy masking and bias mitigation.
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chapters (14)
- 0:00Introduction to talk
- 0:50Global mental health crisis
- 3:22Why knowledge graphs matter
- 5:48Mental health ontology with graphs
- 7:54Building knowledge graphs for diagnosis
- 10:24Privacy and fairness in graphs
- 11:28Neo4j and GCP partnership
- 12:56Vertex AI chatbot integration
- 17:18Bias removal in graph data
- 18:23LangChain and Neo4j setup
- 20:31Grounding chatbot responses
- 22:22LLM capabilities on Vertex AI
- 23:32Practical chatbot example
- 27:24Severity-based recommendations
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