FremyCompany/AGCT-Dataset

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The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: other, from the tagEvidence: README.md at 1c69bbf
Licence files: No licence file at the top level of the repositoryEvidence: file list at 1c69bbf
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@inproceedings{remy-etal-2023-automatic,
    title = "Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge",
    author = "Remy, Fran{\c{c}}ois  and
      Demuynck, Kris  and
      Demeester, Thomas",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.23",
    doi = "10.18653/v1/2023.bionlp-1.23",
    pages = "265--272",
    abstract = "Background: More than 400.000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are not always readily interpretable by non-experts, or patients looking at their own electronic health records (EHR). Clear definitions or descriptions in understandable language or often not available. Therefore, generating human-readable definitions for biomedical concepts might help make the information they encode more accessible and understandable to a wider public. Objective: In this article, we introduce the Automatic Glossary of Clinical Terminology (AGCT), a large-scale biomedical dictionary of clinical concepts generated using high-quality information extracted from t

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dataset_licence({ "id": "FremyCompany/AGCT-Dataset" })
signal: "unclear"
stated_licence: "other"
sources: 0