| | --- |
| | language: |
| | - en |
| | bigbio_language: |
| | - English |
| | license: other |
| | multilinguality: monolingual |
| | bigbio_license_short_name: PHYSIONET_LICENSE_1p5 |
| | pretty_name: MedNLI |
| | homepage: https://physionet.org/content/mednli/1.0.0/ |
| | bigbio_pubmed: false |
| | bigbio_public: false |
| | bigbio_tasks: |
| | - TEXTUAL_ENTAILMENT |
| | paperswithcode_id: mednli |
| | --- |
| | |
| |
|
| | # Dataset Card for MedNLI |
| |
|
| | ## Dataset Description |
| |
|
| | - **Homepage:** https://physionet.org/content/mednli/1.0.0/ |
| | - **Pubmed:** False |
| | - **Public:** False |
| | - **Tasks:** TE |
| |
|
| |
|
| | State of the art models using deep neural networks have become very good in learning an accurate |
| | mapping from inputs to outputs. However, they still lack generalization capabilities in conditions |
| | that differ from the ones encountered during training. This is even more challenging in specialized, |
| | and knowledge intensive domains, where training data is limited. To address this gap, we introduce |
| | MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), |
| | grounded in the medical history of patients. As the source of premise sentences, we used the |
| | MIMIC-III. More specifically, to minimize the risks to patient privacy, we worked with clinical |
| | notes corresponding to the deceased patients. The clinicians in our team suggested the Past Medical |
| | History to be the most informative section of a clinical note, from which useful inferences can be |
| | drawn about the patient. |
| |
|
| |
|
| | ## Citation Information |
| |
|
| | ``` |
| | @misc{https://doi.org/10.13026/c2rs98, |
| | title = {MedNLI — A Natural Language Inference Dataset For The Clinical Domain}, |
| | author = {Shivade, Chaitanya}, |
| | year = 2017, |
| | publisher = {physionet.org}, |
| | doi = {10.13026/C2RS98}, |
| | url = {https://physionet.org/content/mednli/} |
| | } |
| | ``` |
| |
|
