OTAR3088/PHEE_eval_v1.conll

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.

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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

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Intended Use

Primary Use

• Supervised NER training for biomedical NLP tasks

Not Intended For

• Clinical or patient-level decision making

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Dataset Structure

• Language: English
• Splits: Train / Test / Validation
• Features: Text field, BIO label
• Labels: Adev ~ 'Adverse Event'

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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

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Limitations

• Relatively small corpus size compared to large-scale pretraining datasets
• Specific to medical case report abstracts only

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Ethical Considerations

• All content originates from publicly available, open-access scientific datasets
• No personal, clinical, or identifiable patient information is included

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Citation

If you use this dataset, please cite the original publication:

@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}
}