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EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases

arXiv 2510.00549 ↗First posted 1 Oct 2025cs.DB cs.AIcited by 0

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Abstract (arXiv)

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/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 47 references that are in the map. First posted on arXiv 1 Oct 2025.

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28 of 47 references.

References · 28

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16 more
  1. 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 121
  2. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 101
  3. 2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
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  5. 2024EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Recordscited by 75
  6. 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Eventscited by 46
  7. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 42
  8. 2019Benchmarking machine learning models on multi-centre eICU critical care datasetcited by 35
  9. 2023Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical MLcited by 28
  10. 2023Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attentioncited by 25
  11. 2021Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embeddingcited by 24
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  13. 2024ACES: Automatic Cohort Extraction System for Event-Stream Datasetscited by 8
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  15. 2024EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Recordscited by 5
  16. 2022Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolutioncited by 1

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