Augmenting large language models with clinical knowledge graph for personalized perioperative fluid therapy question answering.

Personalized perioperative fluid therapy is important for reducing postoperative complications and adverse outcomes. Although large language models (LLMs) show promise in healthcare, their application in fluid therapy remains challenged by hallucinations, limited domain-specific knowledge, and insufficient personalization. To address these limitations, Retrieval-Augmented Generation (RAG) is an effective method, while Knowledge Graphs (KGs) provide more accurate and reliable information. In this paper, we constructed a Personalized Fluid Therapy Knowledge Graph (PFTKG) comprising 6,490 entities and 15,687 relationships, and adapted GraphRAG, a graph-based RAG strategy that employs community detection and recursive summarization to support finding-level retrieval of clinically relevant information. We compared GraphRAG with document-based retrieval-augmented generation (DocRAG) and mainstream prompting strategies, including Vanilla, Chain-of-Thought (CoT), and Reflection-of-Thoughts (RoT), across three LLMs: GPT-4o, Claude Opus 4, and Gemini 2.5 Pro. Performance was evaluated using a 300-question knowledge-based question set and a 262-question retrospective case-based question set derived from 206 abdominal surgery patients. Evaluation included accuracy, honesty, error composition, response length, and response time. On the knowledge-based question set, GraphRAG achieved the highest average accuracy: 96.89% for multiple-choice questions and 66.44% for open-ended questions. On the retrospective case-based question set, GraphRAG also showed the strongest overall performance, with an average accuracy of 71.12%, compared with 62.47% for DocRAG, 54.20% for CoT, 52.67% for Vanilla, and 52.54% for RoT. Adding a "Don't know" option increased explicit acknowledgment of uncertainty, and GraphRAG reduced context-irrelevant errors compared with DocRAG. These results support GraphRAG as a domain-adapted retrieval strategy for personalized perioperative fluid therapy question answering. By integrating a clinical knowledge graph with hierarchical summarization and finding-level retrieval, it improved answer accuracy and promoted more conservative responses under uncertainty in both knowledge-based and retrospective case-based evaluations, supporting its use in future clinically integrated studies.