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.
@article{arxiv.2409.12853,
title = {A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights},
author = {Hakan T. Otal and Stephen V. Faraone and M. Abdullah Canbaz},
journal= {arXiv preprint arXiv:2409.12853},
year = {2025}
}