使用大型语言模型生成纵向合成临床笔记的流水线
A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models
AI总结：
提出一种模块化流水线，结合结构化患者生成、半结构化旅程模拟和LLM生成非结构化笔记，生成纵向一致的合成临床数据集，支持临床AI开发并避免隐私风险。
摘要：
合成数据越来越多地被用于在访问真实世界数据受限的领域中支持AI系统的开发和评估。在医疗保健领域，临床文档因其敏感性而面临特殊挑战。本文介绍了一个合成临床笔记流水线和数据集，旨在支持临床AI工具的开发，同时避免与真实患者数据相关的隐私风险。该数据集使用模块化流水线生成，该流水线结合了结构化患者生成、半结构化患者旅程模拟以及使用大型语言模型生成非结构化临床笔记。该流水线旨在优先考虑纵向患者记录的内部一致性，同时捕捉写作风格、笔记结构和临床细节的变化。额外的机制，包括基于LLM的验证和增强步骤，用于提高生成笔记的忠实度、真实性和多样性。我们发布了一个包含70名合成患者的数据集，每位患者有20-50份临床笔记，涵盖完整的医院旅程。该数据集以多个验证级别提供，使用户能够根据其用例平衡真实性和可扩展性。该数据集支持临床AI系统的开发、测试和评估，包括摘要工具、编码模型和决策支持系统，而无需依赖真实患者数据。
英文摘要：
Synthetic data is increasingly used to enable the development and evaluation of AI systems in domains where access to real-world data is restricted. In healthcare, clinical documentation presents particular challenges due to its sensitivity. This work introduces a synthetic clinical notes pipeline and dataset designed to support the development of clinical AI tools while avoiding the privacy risks associated with real patient data. The dataset is generated using a modular pipeline that combines structured patient generation, semi-structured patient journey simulation, and unstructured clinical note generation using large language models. The pipeline is designed to prioritise internal consistency across longitudinal patient records, while also capturing variation in writing style, note structure, and clinical detail. Additional mechanisms, including LLM-based validation and augmentation steps, are used to improve faithfulness, realism, and diversity of the generated notes. We release a dataset of 70 synthetic patients, each associated with 20-50 clinical notes spanning a full hospital journey. The dataset is provided at multiple levels of validation, enabling users to balance realism and scalability depending on their use case. This dataset supports the development, testing, and evaluation of clinical AI systems, including summarisation tools, coding models, and decision support systems, without reliance on real patient data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
