标题： KERAP：一种基于多智能体大语言模型的增强知识推理方法，用于准确的零样本诊断预测 显示英文标题

标题： KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs

摘要： 医学诊断预测在疾病检测和个性化医疗中起着关键作用。 尽管机器学习（ML）模型已被广泛用于此任务，但它们对监督训练的依赖限制了其在未见过病例上的泛化能力，特别是考虑到获取大型标记数据集的高成本。 大型语言模型（LLMs）在利用语言能力和生物医学知识进行诊断预测方面显示出前景。 然而，它们常常存在幻觉问题，缺乏结构化的医学推理，并产生无用的输出。 为了解决这些挑战，我们提出了KERAP，一种基于知识图谱（KG）的增强推理方法，通过多智能体架构改进基于LLM的诊断预测。 我们的框架包括一个用于属性映射的链接代理、一个用于结构化知识提取的检索代理，以及一个迭代优化诊断预测的预测代理。 实验结果表明，KERAP能高效地提高诊断的可靠性，为零样本医学诊断预测提供了一种可扩展且可解释的解决方案。 显示英文摘要

摘要： Medical diagnosis prediction plays a critical role in disease detection and personalized healthcare. While machine learning (ML) models have been widely adopted for this task, their reliance on supervised training limits their ability to generalize to unseen cases, particularly given the high cost of acquiring large, labeled datasets. Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction. However, they often suffer from hallucinations, lack structured medical reasoning, and produce useless outputs. To address these challenges, we propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture. Our framework consists of a linkage agent for attribute mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions. Experimental results demonstrate that KERAP enhances diagnostic reliability efficiently, offering a scalable and interpretable solution for zero-shot medical diagnosis prediction.