ADHD研究的新视角：基于大语言模型的知识图谱构建与网络分析洞察
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
AI总结：
针对ADHD研究因症状复杂、影响因素多样而存在的挑战，该研究借助LLMs整合文献与临床数据构建ADHD知识图谱，通过网络分析识别核心节点与关系，构建可用于Graph-RAG聊天机器人的KG，为ADHD研究和临床应用提供新工具与洞察。
摘要：
注意缺陷多动障碍（ADHD）因其复杂的症状表现和多样的影响因素，是一种研究难度较大的疾病。为探索如何深入理解该疾病，我们借助前沿大语言模型（LLMs）整合科学文献与临床数据，构建了全面的ADHD知识图谱（KG），并对其进行网络分析。通过k核技术等分析方法，我们识别出理解该疾病核心的关键节点和关系。基于这些发现，我们构建了可用于LLMs驱动的上下文感知聊天机器人（Graph-RAG）的知识图谱，不仅推动了对ADHD的理解，还为研究和临床应用提供了强大工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
