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

Hakan T. Otal, Stephen V. Faraone, M. Abdullah Canbaz
9/19/2024
cs.SIcs.CL

No official code found. Best implementation: AI-in-Complex-Systems-Lab/ADHD-KnowledgeGraph (13 stars, last commit 2024-11-26).

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.

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Code Implementations(1)

AI-in-Complex-Systems-Lab/ADHD-KnowledgeGraphCommunity100%

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

135Python, Jupyter NotebookJul 31, 20241 years ago

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Cite this paper

@article{otal2024perspective,
  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},
  year   = {2024},
  eprint = {2409.12853},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.12853v2}
}

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