Electronic health records (EHRs) house crucial patient data in clinical notes. As these notes grow in volume and complexity, manual extraction becomes challenging. This work introduces a natural language interface using large language models (LLMs) for dynamic question-answering on clinical notes. Our chatbot, powered by Langchain and transformer-based LLMs, allows users to query in natural language, receiving relevant answers from clinical notes. Experiments, utilizing various embedding models and advanced LLMs, show Wizard Vicuna's superior accuracy, albeit with high compute demands. Model optimization, including weight quantization, improves latency by approximately 48 times. Promising results indicate potential, yet challenges such as model hallucinations and limited diverse medical case evaluations remain. Addressing these gaps is crucial for unlocking the value in clinical notes and advancing AI-driven clinical decision-making.

翻译：电子健康记录（EHRs）中蕴含临床记录中的关键患者数据。随着这些记录数量和复杂性的增长，人工提取信息变得愈发困难。本研究提出一种基于大语言模型（LLMs）的自然语言界面，用于临床记录的动态问答。我们开发的聊天机器人基于Langchain和基于Transformer架构的大语言模型，允许用户以自然语言进行查询，并从临床记录中获取相关答案。实验采用多种嵌入模型与先进大语言模型，结果显示Wizard Vicuna在准确性方面表现优异，但计算需求较高。通过权重量化等模型优化技术，推理延迟降低了约48倍。研究结果展现了潜力，但仍存在模型幻觉、多样化医疗案例评估有限等挑战。解决这些不足对于释放临床记录价值、推动基于AI的临床决策至关重要。