Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers, risking diagnostic inaccuracies. While Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the clinical diagnostic reasoning process. We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge. Our method negates the need for pre-training and instead leverages the KG as an auxiliary instrument aiding in the interpretation and summarization of complex medical concepts. Using real-world hospital datasets, our experimental results demonstrate that the proposed approach of combining LLMs with KG has the potential to improve the accuracy of automated diagnosis generation. More importantly, our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems.

翻译：电子健康记录（EHR）及常规文档实践在患者日常护理中发挥关键作用，提供健康、诊断和治疗的全面记录。然而，复杂冗长的EHR叙述加重了医疗从业者的负担，可能导致诊断偏差。尽管大型语言模型（LLM）在多种语言任务中展现出潜力，其在医疗领域的应用仍需确保诊断错误最小化并避免患者伤害。本文提出一种创新方法，通过整合医学知识图谱（KG）及受临床诊断推理过程启发的新型图模型Dr.Knows，提升LLM在自动诊断生成中的能力。我们从美国国立医学图书馆的统一医学语言系统（UMLS）——一个强大的生物医学知识库——构建知识图谱。该方法无需预训练，而是将KG作为辅助工具，协助解读和总结复杂医学概念。基于真实医院数据集的实验结果表明，本文提出的LLM与KG结合方法具有提升自动诊断生成准确性的潜力。更重要的是，该方法提供了可解释的诊断路径，使我们更接近AI增强型诊断决策支持系统的实现。