用AI代理自动整合多源数据，提升临床数据标准化效率。
Interactive Data Harmonization with LLM Agents: Opportunities and Challenges
- 构建基于大模型的交互式系统，自动设计数据融合流程
- 在临床数据场景中实现标准格式转换，支持可复用管道生成
- 适合数据科学家与领域专家协作，降低数据整合门槛
数据整合是将来自不同来源的数据进行统一的重要任务。尽管该领域已有多年研究，但仍因模式不匹配、术语差异和采集方法不同而耗时且困难。本文提出以智能体方式推进数据整合，展示一种名为Harmonia的系统，结合大模型推理、交互界面和数据整合原语库，自动合成数据整合流水线。我们在临床数据整合场景中验证该系统，能够帮助用户交互式创建可复用的标准化转换管道。最后，我们讨论当前挑战与开放问题，并提出未来研究方向。
原文摘要 · Abstract (English)
Data harmonization is an essential task that entails integrating datasets from diverse sources. Despite years of research in this area, it remains a time-consuming and challenging task due to schema mismatches, varying terminologies, and differences in data collection methodologies. This paper presents the case for agentic data harmonization as a means to both empower experts to harmonize their data and to streamline the process. We introduce Harmonia, a system that combines LLM-based reasoning, an interactive user interface, and a library of data harmonization primitives to automate the synthesis of data harmonization pipelines. We demonstrate Harmonia in a clinical data harmonization scenario, where it helps to interactively create reusable pipelines that map datasets to a standard format. Finally, we discuss challenges and open problems, and suggest research directions for advancing our vision.
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