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

• URL: http://arxiv.org/abs/2409.12853v2
• Date: Sat, 19 Oct 2024 04:21:12 GMT
• Title: A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
• Authors: Hakan T. Otal, Stephen V. Faraone, M. Abdullah Canbaz,
• Abstract summary: 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. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder.
• Score: 0.4915744683251151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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