Add paper link, project page, and task category

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by nielsr HF Staff - opened
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  1. README.md +40 -0
README.md CHANGED
@@ -23,4 +23,44 @@ configs:
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  path: data/train-*
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  - split: dev
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  path: data/dev-*
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  path: data/train-*
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  - split: dev
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  path: data/dev-*
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+ language:
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+ - en
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+ task_categories:
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+ - text-classification
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+ tags:
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+ - medical
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+ - triage
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  ---
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+ ---
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+
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+ # PMR-Bench
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+
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+ [**Project Page**](https://tinyurl.com/Patient-Message-Triage) | [**Paper**](https://huggingface.co/papers/2601.13178)
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+ 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.
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+
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+ ## Dataset Summary
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+
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+ 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:
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+ - **Unstructured patient-written messages**: Direct communication from patients.
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+ - **Electronic Health Record (EHR) data**: Real medical context provided alongside messages.
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+ - **Expert Guidance**: Automated data annotation strategies that provide in-domain guidance for training LLMs.
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+
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+ 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.
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+ ## Task Description
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+ 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.
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+
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+ ## Citation
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+
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+ If you use this dataset in your research, please cite:
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+
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+ ```bibtex
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+ @article{gatto2026medical,
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+ title={Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages},
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+ author={Gatto, Joseph and Seegmiller, Parker and Burdick, Timothy and Resnik, Philip and Rahat, Roshnik and DeLozier, Sarah and Preum, Sarah M.},
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+ journal={arXiv preprint arXiv:2601.13178},
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+ year={2026}
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+ }
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+ ```