用智能代理端到端生成符合标准的医疗数据资源，提升临床文本结构化能力。
Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes
- 基于大模型代理与代码执行，直接从自由文本生成合规FHIR资源
- 在合成与真实临床数据上表现接近人工基准，结构一致性高
- 适合需要跨机构数据整合的医疗AI研发人员使用
为促进临床数据集成与医疗服务，HL7 FHIR标准已成为复杂健康数据互操作性的理想格式。以往将自由文本自动转换为结构化FHIR资源的方法多针对特定任务，依赖模块化流程或指令微调的LLM与受限解码，常因泛化能力差和结构不符而受限。本文提出一种由大模型代理、代码执行与医疗术语数据库驱动的端到端框架Infherno，以解决上述问题。该框架严格遵循FHIR文档模式，在合成与临床数据集上的表现可媲美人类基准。系统支持自定义与合成数据前端，兼容本地及专有模型，助力医疗机构间的数据集成与互操作。评估中Gemini 2.5-Pro表现最优，但真实标注数据的获取仍存在模糊性与可行性挑战。
原文摘要 · Abstract (English)
For clinical data integration and healthcare services, the HL7 FHIR standard has established itself as a desirable format for interoperability between complex health data. Previous attempts at automating the translation from free-form clinical notes into structured FHIR resources address narrowly defined tasks and rely on modular approaches or LLMs with instruction tuning and constrained decoding. As those solutions frequently suffer from limited generalizability and structural inconformity, we propose an end-to-end framework powered by LLM agents, code execution, and healthcare terminology database tools to address these issues. Our solution, called Infherno, is designed to adhere to the FHIR document schema and competes well with a human baseline in predicting FHIR resources from unstructured text. The implementation features a front end for custom and synthetic data and both local and proprietary models, supporting clinical data integration processes and interoperability across institutions. Gemini 2.5-Pro excels in our evaluation on synthetic and clinical datasets, yet ambiguity and feasibility of collecting ground-truth data remain open problems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。