A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
<|reference_start|>A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights: 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 developed a context-aware chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), 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.<|reference_end|>
@article{otal2024a,
    title={A New Perspective on ADHD Research: Knowledge Graph Construction with
  LLMs and Network Based Insights},
    author={Hakan T. Otal, Stephen V. Faraone and M. Abdullah Canbaz},
    journal={arXiv preprint arXiv:2409.12853},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12853},
    primaryClass={cs.SI cs.CL}
}