A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights

TL;DR

This paper constructs a comprehensive knowledge graph of ADHD using large language models and network analysis, revealing key insights and enabling advanced clinical and research tools like a context-aware chatbot.

Contribution

It introduces a novel method of integrating literature and clinical data into a knowledge graph for ADHD using LLMs and network analysis techniques.

Findings

Identified critical nodes and relationships in ADHD knowledge graph

Developed a context-aware chatbot powered by the knowledge graph

Enhanced understanding of ADHD's complex symptomatology

Abstract

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.