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---
license: mit
task_categories:
- feature-extraction
language:
- en
tags:
- NER
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-Subject.Age
'2': I-Subject.Age
'3': B-Subject.Disorder
'4': I-Subject.Disorder
'5': B-Subject.Gender
'6': I-Subject.Gender
'7': B-Subject.Population
'8': I-Subject.Population
'9': B-Subject.Race
'10': I-Subject.Race
'11': B-Treatment.Disorder
'12': I-Treatment.Disorder
'13': B-Treatment.Dosage
'14': I-Treatment.Dosage
'15': B-Treatment.Drug
'16': I-Treatment.Drug
'17': B-Treatment.Duration
'18': I-Treatment.Duration
'19': B-Treatment.Freq
'20': I-Treatment.Freq
'21': B-Treatment.Route
'22': I-Treatment.Route
'23': B-Treatment.Time_elapsed
'24': I-Treatment.Time_elapsed
'25': B-adverse event
'26': I-adverse event
'27': B-potential therapeutic event
'28': I-potential therapeutic event
splits:
- name: train
num_bytes: 1151668
num_examples: 2898
- name: test
num_bytes: 389394
num_examples: 968
- name: validation
num_bytes: 384892
num_examples: 961
download_size: 412980
dataset_size: 1925954
---
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']
```code
@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."
}
``` |