| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - text-generation |
| | language: |
| | - en |
| | tags: |
| | - medical |
| | - clinical |
| | - EHR |
| | --- |
| | |
| | # AGENTEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization |
| |
|
| | [**Paper**](https://huggingface.co/papers/2601.13918) | [**Code**](https://github.com/BlueZeros/AgentEHR) |
| |
|
| | **AGENTEHR** is a novel benchmark designed to bridge the gap between idealized experimental settings and realistic clinical environments. Unlike previous tasks that focus on factual retrieval, AGENTEHR challenges agents to perform complex **clinical decision-making**—such as diagnosis and treatment planning—directly within raw, high-noise EHR databases. |
| |
|
| | ## 💡 Key Features |
| | * **Realistic Clinical Benchmark**: Covers six core tasks (Diagnoses, Labevents, Microbiology, Prescriptions, Procedures, and Transfers) spanning the entire patient hospitalization lifecycle. |
| | * **Toolbox MCP Server**: A standardized interface providing agents access to over **19 specialized tools**, including SQL execution, temporal filtering, and semantic search. |
| | * **Retrospective Reasoning**: Supports the evaluation of frameworks that re-evaluate interaction history to capture latent correlations and ensure logical coherence. |
| | * **Experience Memory Bank**: Facilitates strategies that crystallize successful approaches into an external memory bank. |
| |
|
| | ## 📊 Benchmark Structure |
| | AGENTEHR is organized into three experimental subsets based on MIMIC-IV and MIMIC-III to evaluate generalization and robustness: |
| |
|
| | | Subset | Distribution Type | Description | |
| | | :--- | :--- | :--- | |
| | | **MIMIC-IV-Common** | In-Distribution | Primary benchmark assessing standard clinical reasoning capabilities on prevalent conditions. | |
| | | **MIMIC-IV-Rare** | Label-Shift OOD | Evaluates the agent's ability to handle low-prevalence diseases where parametric knowledge is weaker. | |
| | | **MIMIC-III** | Systemic-Shift OOD | Presents fundamental differences in table schema and higher recording density/noise. | |
| |
|
| | ## ⚡ Quick Start |
| |
|
| | ### Environment Setup |
| | To use this benchmark with the official code, follow these steps: |
| | ```bash |
| | git clone https://github.com/BlueZeros/AgentEHR.git |
| | cd AgentEHR |
| | pip install -r requirements.txt |
| | pip install -U vllm |
| | ``` |
| |
|
| | ### Database Preparation |
| | Download the dataset from this repository. Copy the `EHRAgentBench` and `MIMICIIIAgentBench` folders into the `./data` folder in your root directory of the cloned repository. |
| |
|
| | ## Citation |
| | If you find our work helpful, please cite our paper: |
| | ```bibtex |
| | @article{liao2026agentehr, |
| | title={AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization}, |
| | author={Yusheng Liao and Chuan Xuan and Yutong Cai and Lina Yang and Zhe Chen and Yanfeng Wang and Yu Wang}, |
| | journal={arXiv preprint arXiv:2601.13918}, |
| | year={2026} |
| | } |
| | ``` |
