File size: 2,302 Bytes
a7bc980 1e833e9 a7bc980 1e833e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | ---
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}
}
``` |