ArticleProceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing2024
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records.
Article in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Skill-guided agentic reasoning enables longitudinal synthesis of myeloma records at expert-level concordance.NPJ precision oncology · 2026Article
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- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
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- Large language models are poor clinical administrators: An evaluation of structured queries in real-world electronic health records.PLOS digital health · 2026Article
- A scalable framework for evaluating health language models.NPJ digital medicine · 2026Article
- Transforming wearable data into personal health insights using large language model agents.Nature communications · 2026Article
- MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling.NPJ digital medicine · 2025Article
- Enhancing Large Language Models With AI Agents for Chronic Gastritis Management: Comprehensive Comparative Study.JMIR medical informatics · 2025Article
- The rise and potential opportunities of large language model agents in bioinformatics and biomedicine.Briefings in bioinformatics · 2025Review
- Critique of impure reason: Unveiling the reasoning behaviour of medical large language models.eLife · 2025Article
- AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning.Nature communications · 2025Article
- AI Agents in Clinical Medicine: A Systematic Review.medRxiv : the preprint server for health sciences · 2025Article
- A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Train-Time and Test-Time Computation in Large Language Models for Error Detection and Correction in Electronic Medical Records: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Performance of single-agent and multi-agent language models in Spanish language medical competency exams.BMC medical education · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Clinicians often rely on data engineers to retrieve complex patient information from electronic health record (EHR) systems, a process that is both inefficient and time-consuming. We propose EHRAgent, a large language model (LLM) agent empowered with accumulative domain knowledge and robust coding capability. EHRAgent enables autonomous code generation and execution to facilitate clinicians in directly interacting with EHRs using natural language. Specifically, we formulate a multi-tabular reasoning task based on EHRs as a tool-use planning process, efficiently decomposing a complex task into a sequence of manageable actions with external toolsets. We first inject relevant medical information to enable EHRAgent to effectively reason about the given query, identifying and extracting the required records from the appropriate tables. By integrating interactive coding and execution feedback, EHRAgent then effectively learns from error messages and iteratively improves its originally generated code. Experiments on three real-world EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate, verifying its strong capacity to tackle complex clinical tasks with minimal demonstrations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.