EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
This addresses scalability and reproducibility issues in clinical prediction models for healthcare researchers and practitioners, though it appears incremental as it builds on existing agent and language model techniques.
The paper tackles the problem of manual, hardcoded pipelines for extracting structured data from Electronic Medical Records (EMRs) by introducing EMR-AGENT, an agent-based framework that automates cohort selection, feature extraction, and code mapping using language model-driven interaction, demonstrating strong performance and generalization across three EMR databases.
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/