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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: apache-2.0 |
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multilinguality: monolingual |
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bigbio_license_shortname: APACHE_2p0 |
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pretty_name: EHR-Rel |
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homepage: https://github.com/babylonhealth/EHR-Rel |
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bigbio_pubmed: False |
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bigbio_public: True |
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bigbio_tasks: |
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- SEMANTIC_SIMILARITY |
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--- |
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# Dataset Card for EHR-Rel |
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## Dataset Description |
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- **Homepage:** https://github.com/babylonhealth/EHR-Rel |
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- **Pubmed:** False |
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- **Public:** True |
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- **Tasks:** STS |
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EHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more |
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than the largest existing dataset. Instead of manually selecting and pairing concepts as done in previous work, |
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the dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task. |
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A detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets. |
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## Citation Information |
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``` |
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@inproceedings{schulz-etal-2020-biomedical, |
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title = {Biomedical Concept Relatedness {--} A large {EHR}-based benchmark}, |
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author = {Schulz, Claudia and |
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Levy-Kramer, Josh and |
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Van Assel, Camille and |
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Kepes, Miklos and |
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Hammerla, Nils}, |
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booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, |
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month = {dec}, |
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year = {2020}, |
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address = {Barcelona, Spain (Online)}, |
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publisher = {International Committee on Computational Linguistics}, |
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url = {https://aclanthology.org/2020.coling-main.577}, |
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doi = {10.18653/v1/2020.coling-main.577}, |
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pages = {6565--6575}, |
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} |
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``` |
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