Dataset:

Task Categories: other
Languages: en
Multilinguality: monolingual
Size Categories: n<1K
Licenses: unknown
Language Creators: expert-generated
Annotations Creators: expert-generated
Source Datasets: original

Dataset Card Creation Guide

Dataset Summary

A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate

Supported Tasks and Leaderboards

Medical abbreviation disambiguation

Languages

English (en)

Dataset Structure

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Data Instances

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Data Fields

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Data Splits

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Dataset Creation

Curation Rationale

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Source Data

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Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@inproceedings{wen-etal-2020-medal,
    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
    author = "Wen, Zhi  and
      Lu, Xing Han  and
      Reddy, Siva",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
    pages = "130--135",
    abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}

Contributions

Thanks to @Narsil for adding this dataset.

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Repository:
https://github.com/BruceWen120/medal
Paper:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/
Dataset (Kaggle):
https://www.kaggle.com/xhlulu/medal-emnlp
Dataset (Zenodo):
https://zenodo.org/record/4265632
Pretrained model:
https://huggingface.co/xhlu/electra-medal

Models trained or fine-tuned on medal

None yet