Datasets:

Task Categories: text-retrieval
Languages: en
Multilinguality: monolingual
Size Categories: 1K<n<10K
Licenses: mit
Language Creators: expert-generated
Annotations Creators: expert-generated
Source Datasets: original

Dataset Card Creation Guide

Dataset Summary

Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.

The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.

The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.

Supported Tasks and Leaderboards

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Languages

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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{lehman-etal-2019-inferring,
    title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
    author = "Lehman, Eric  and
      DeYoung, Jay  and
      Barzilay, Regina  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1371",
    pages = "3705--3717",
}

Contributions

Thanks to @Narsil for adding this dataset.

Models trained or fine-tuned on evidence_infer_treatment

None yet