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（EHR）在临床笔记中存储着关键的患者数据。随着这些笔记在数量和复杂性上的增长，人工提取变得日益困难。本研究引入了一种利用大型语言模型（LLMs）的自然语言界面，用于对临床笔记进行动态问答。我们基于Langchain和基于Transformer的LLMs开发的聊天机器人，允许用户以自然语言进行查询，并从临床笔记中获取相关答案。实验采用了多种嵌入模型和先进的LLMs，结果显示Wizard Vicuna模型具有更高的准确性，尽管其计算需求较高。模型优化（包括权重量化）将延迟降低了约48倍。这些有前景的结果表明了该方法的潜力，但仍存在模型幻觉和多样医疗案例评估有限等挑战。解决这些不足对于释放临床笔记的价值及推动人工智能驱动的临床决策至关重要。