|
|
--- |
|
|
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} |
|
|
} |
|
|
``` |
