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
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:
@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}
}