Datasets:
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language: en
license: mit
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: PHEE validation data
tags:
- pharmacovigilance
- adverse-event
- medical
- ner
---
# PHEE validation data
## Dataset Description
This dataset contains sentences derived from medical case report abstracts
curated for adverse events. Split data and CoNLL formatting allows for the
**training of language models**, for **named entity recognition.** The dataset
includes entity annotations or labels. This subsect is the validation split.
The creation of the original PHEE dataset is detailed at:
> Sun, Z., Li, J., Pergola, G., Wallace, B. C., John, B., Greene, N., Kim, J.,
> & He, Y. (2022). PHEE: A dataset for pharmacovigilance event extraction from
> text. arXiv preprint arXiv:2210.12560.
> https://arxiv.org/pdf/2210.12560.
---
## Source Data
The port of the original PHEE dataset used for our purposes is detailed here:
Original source repository:
https://huggingface.co/datasets/sarus-tech/phee
---
## Intended Use
### Primary Use
- Supervised NER training for biomedical NLP tasks
### Not Intended For
- Clinical or patient-level decision making
---
## Dataset Structure
- **Language:** English
- **Splits:** Train / Test / Validation
- **Features:** Text field, BIO label
- **Labels:** Adev ~ 'Adverse Event'
---
## Preprocessing
- Sentence-level segmentation is enforced
- Annotations carried out by 15 annotators in data's original creation
- Present dataset split into train / test / val
- Present dataset labeled in the IOB CoNLL format
---
## Limitations
- Relatively small corpus size compared to large-scale pretraining datasets
- Specific to medical case report abstracts only
---
## Ethical Considerations
- All content originates from publicly available, open-access scientific datasets
- No personal, clinical, or identifiable patient information is included
---
## Citation
If you use this dataset, please cite the original publication:
```bibtex
@article{sun2022phee,
title = {PHEE: A dataset for pharmacovigilance event extraction from text},
author = {Sun, Z., Li, J., Pergola, G., Wallace, B. C., John, B., Greene, N., Kim, J., & He, Y.},
journal = {arXiv},
year = {2022},
doi = {preprint arXiv:2210.12560}
}
```
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