用大模型构建注意力缺陷图谱，发现关键节点与关系。
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
- 结合文献与临床数据，用大模型构建 ADHD 知识图谱。
- 通过核心网络分析，识别出疾病关键节点与关联路径。
- 支持上下文感知对话，助力科研与临床决策。
注意力缺陷/多动障碍（ADHD）因症状复杂、成因多样，研究难度高。本文借助前沿大语言模型，整合科学文献与临床数据，构建了全面的 ADHD 知识图谱，并基于该图谱开展网络分析，运用 k-core 技术识别出对理解该病至关重要的关键节点与核心关系。在此基础上，我们进一步构建了一个可用于上下文感知聊天机器人的知识图谱系统（Graph-RAG），实现与大语言模型的精准交互。本知识图谱不仅深化了对 ADHD 的认知，也为研究与临床应用提供了强大工具。
原文摘要 · Abstract (English)
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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