A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
This work addresses the problem of gaining deeper insights into ADHD for researchers and clinicians, though it is incremental as it applies existing methods to this specific domain.
The researchers tackled the challenge of studying ADHD's complex symptomatology by constructing a knowledge graph using large language models and performing network analysis, which identified critical nodes and relationships to advance understanding and enable a context-aware chatbot for research and clinical use.
Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD, constructed by integrating scientific literature and clinical data with the help of cutting-edge large language models. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder. Building on these findings, we curated a knowledge graph that is usable in a context-aware chatbot (Graph-RAG) with Large Language Models (LLMs), enabling accurate and informed interactions. Our knowledge graph not only advances the understanding of ADHD but also provides a powerful tool for research and clinical applications.