用AI代理自动从病历库提取数据，省去人工写规则的麻烦。
EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
- 用语言模型驱动的智能代理，动态交互查询病历库
- 在三个数据库上实现跨库通用的数据提取，准确率高
- 适合医疗数据工程师和临床研究者快速搭建分析流程
临床预测模型依赖从电子病历（EMR）中提取结构化数据，但这一过程仍主要依赖针对特定数据库的手动规则，限制了可扩展性、可复现性和跨机构通用性。为此，我们提出EMR-AGENT（自动化通用提取与导航工具），一个基于智能体的框架，用动态语言模型交互取代人工编写规则，实现队列选择、特征提取和编码映射的自动化。该框架通过迭代式查询数据库，结合模式和文档理解，使用SQL不仅检索数据，还作为观察与决策工具，无需手工设计的模式专用逻辑。为支持严格评估，我们构建了针对MIMIC-III、eICU、SICdb三个数据库的基准测试代码库，涵盖已见和未见模式场景。结果表明，该方法在多数据库上表现优异且具备良好泛化能力，验证了此前需专家设计流程的自动化可行性。代码将公开于https://github.com/AITRICS/EMR-AGENT/tree/main。演示页面：https://anonymoususer-max600.github.io/EMR_AGENT/
原文摘要 · Abstract (English)
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/
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