SKG-LLM represents a breakthrough in organizing stroke research data by building comprehensive knowledge graphs from biomedical literature using GPT-4.

• Employs mathematical modeling and LLMs to extract and organize complex relationships in stroke research
• Leverages GPT-4 for both data pre-processing and embedding extraction
• Creates structured knowledge representations that enhance medical information accessibility
• Demonstrates how AI can transform unstructured medical literature into actionable insights

Why It Matters: This approach could significantly accelerate stroke research by connecting disparate findings, identifying research gaps, and providing clinicians with more comprehensive knowledge resources—potentially improving patient outcomes through better-informed care decisions.

Original Paper: SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models