Information retrieval in pre-hospital care with visualization-oriented natural-language interface via LLMs

With the popularization of Electronic Health Records (EHR), the emergency system has stored a large number of historical dispatch records, which can provide valuable insights for the optimization of current pre-hospital care. However, the inconvenient interaction manner of cur-rent information retrieval systems hinders researchers from exploring these historical records. To address this issue, we propose a novel framework that leverages the language understanding and code generation ability of Large Language Models (LLMs) to build an information retrieval system with Visualization-oriented Natural-language-based Inter-faces (V-NLI). To incorporate both domain-specific and task-related prior knowledge, we generate the instruction datasets based on the ability of closed-source LLMs in a multi-stage manner and conduct supervised fine-tuning on open-source LLMs. We also devised various mechanisms for augmenting the capabilities of open-source LLMs in query interpretation and code generation. To validate the effectiveness and generalizability of our framework, we conducted experiments on a public dataset NLV. More significantly, we performed more detailed experiments on a dataset including over 1 mil-lion pre-hospital emergency historical records in ten years. The performance of our method surpasses all baseline methods and achieves comparable results even with some SOTA closed-source models.