---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: level
    dtype: float64
  - name: expert_comments
    dtype: string
  splits:
  - name: train
    num_bytes: 1089524
    num_examples: 772
  - name: dev
    num_bytes: 127945
    num_examples: 87
  download_size: 751423
  dataset_size: 1217469
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
language:
- en
task_categories:
- text-classification
tags:
- medical
- triage
---
---

# PMR-Bench

[**Project Page**](https://tinyurl.com/Patient-Message-Triage) | [**Paper**](https://huggingface.co/papers/2601.13178)

PMR-Bench (Patient Message Ranking Benchmark) is a large-scale public dataset designed for studying medical triage in the context of asynchronous outpatient portal messages. The benchmark formulates triage as a pairwise inference problem, where models are tasked with determining which of two patient messages is more medically urgent.

## Dataset Summary

The dataset contains 1,569 unique messages and over 2,000 high-quality test pairs for pairwise medical urgency assessment. It emulates real-world medical triage scenarios by including:
- **Unstructured patient-written messages**: Direct communication from patients.
- **Electronic Health Record (EHR) data**: Real medical context provided alongside messages.
- **Expert Guidance**: Automated data annotation strategies that provide in-domain guidance for training LLMs.

The dataset was used to develop and evaluate models like **UrgentReward** and **UrgentSFT**, which outperform standard large language models in sorting physician inboxes by urgency.

## Task Description

The primary task involves a head-to-head tournament-style re-sort of a physician's inbox. Given a pair of messages, the model must predict which one requires more immediate medical attention.

## Citation

If you use this dataset in your research, please cite:

```bibtex
@article{gatto2026medical,
  title={Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages},
  author={Gatto, Joseph and Seegmiller, Parker and Burdick, Timothy and Resnik, Philip and Rahat, Roshnik and DeLozier, Sarah and Preum, Sarah M.},
  journal={arXiv preprint arXiv:2601.13178},
  year={2026}
}
```