EMR-AGENT：从电子病历数据库中自动提取队列与特征
EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
AI总结：
针对电子病历数据提取依赖人工硬编码流水线的痛点，提出基于智能体的EMR-AGENT框架，通过大语言模型驱动的交互自动完成队列与特征提取等任务，在多数据库基准上验证了优异性能与泛化能力。
摘要：
临床预测机器学习模型依赖从电子病历（EMRs）中提取的结构化数据，但这一过程目前仍由针对特定数据库的硬编码流水线主导，涵盖队列定义、特征选择和编码映射环节。这类人工操作限制了可扩展性、可复现性以及跨机构泛化能力。为解决这一问题，我们提出EMR-AGENT（自动化通用提取与导航工具），这是一个基于智能体的框架，以大语言模型驱动的动态交互替代人工规则编写，实现结构化临床数据的提取与标准化。该框架通过与数据库的交互式查询，自动化完成队列筛选、特征提取和编码映射。其模块化智能体可迭代观测查询结果，并基于数据库模式与文档进行推理，不仅将SQL用于数据检索，还将其作为数据库观测与决策的工具，从而无需手工构建针对特定模式的逻辑。为实现严谨评估，我们为三个EMR数据库（MIMIC-III、eICU、SICdb）开发了基准测试代码库，包含已见和未见模式的设置。实验结果表明，该框架在这些数据库上表现优异且具备良好的泛化能力，证明了此前被认为需要专家设计的流程具备自动化可行性。代码将公开发布于https://github.com/AITRICS/EMR-AGENT/tree/main，演示可访问匿名演示页面：https://anonymoususer-max600.github.io/EMR_AGENT/
英文摘要：
Machine learning models for clinical prediction rely on structured data extracted from Electronic Medical Records (EMRs), yet this process remains dominated by hardcoded, database-specific pipelines for cohort definition, feature selection, and code mapping. These manual efforts limit scalability, reproducibility, and cross-institutional generalization. To address this, we introduce EMR-AGENT (Automated Generalized Extraction and Navigation Tool), an agent-based framework that replaces manual rule writing with dynamic, language model-driven interaction to extract and standardize structured clinical data. Our framework automates cohort selection, feature extraction, and code mapping through interactive querying of databases. Our modular agents iteratively observe query results and reason over schema and documentation, using SQL not just for data retrieval but also as a tool for database observation and decision making. This eliminates the need for hand-crafted, schema-specific logic. To enable rigorous evaluation, we develop a benchmarking codebase for three EMR databases (MIMIC-III, eICU, SICdb), including both seen and unseen schema settings. Our results demonstrate strong performance and generalization across these databases, highlighting the feasibility of automating a process previously thought to require expert-driven design. The code will be released publicly at https://github.com/AITRICS/EMR-AGENT/tree/main. For a demonstration, please visit our anonymous demo page: https://anonymoususer-max600.github.io/EMR_AGENT/
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。
