chufangao/PHEE-NER

This is simply a processed version of Pharmacovigilance Event Extraction from Text (PHEE), specialized for NER.

All Credits to https://github.com/ZhaoyueSun/PHEE

names = ['O', 'B-Subject.Age', 'I-Subject.Age', 'B-Subject.Disorder', 'I-Subject.Disorder', 'B-Subject.Gender', 'I-Subject.Gender', 'B-Subject.Population', 'I-Subject.Population', 'B-Subject.Race', 'I-Subject.Race', 'B-Treatment.Disorder', 'I-Treatment.Disorder', 'B-Treatment.Dosage', 'I-Treatment.Dosage', 'B-Treatment.Drug', 'I-Treatment.Drug', 'B-Treatment.Duration', 'I-Treatment.Duration', 'B-Treatment.Freq', 'I-Treatment.Freq', 'B-Treatment.Route', 'I-Treatment.Route', 'B-Treatment.Time_elapsed', 'I-Treatment.Time_elapsed', 'B-adverse event', 'I-adverse event', 'B-potential therapeutic event', 'I-potential therapeutic event']

@inproceedings{sun-etal-2022-phee,
    title = "{PHEE}: A Dataset for Pharmacovigilance Event Extraction from Text",
    author = "Sun, Zhaoyue  and
      Li, Jiazheng  and
      Pergola, Gabriele  and
      Wallace, Byron  and
      John, Bino  and
      Greene, Nigel  and
      Kim, Joseph  and
      He, Yulan",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.376/",
    doi = "10.18653/v1/2022.emnlp-main.376",
    pages = "5571--5587",
    abstract = "The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resources for developing natural language models for this task are scant. We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date. We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects. Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area."
}