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Datasets:
ade_corpus_v2

Tasks:
Text Classification
Token Classification
Sub-tasks:
coreference-resolution
fact-checking
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
1K<n<10K
n<1K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
  - 1K<n<10K
  - n<1K
source_datasets:
  - original
task_categories:
  - text-classification
  - token-classification
task_ids:
  - coreference-resolution
  - fact-checking
pretty_name: Adverse Drug Reaction Data v2
config_names:
  - Ade_corpus_v2_classification
  - Ade_corpus_v2_drug_ade_relation
  - Ade_corpus_v2_drug_dosage_relation
dataset_info:
  - config_name: Ade_corpus_v2_classification
    features:
      - name: text
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': Not-Related
              '1': Related
    splits:
      - name: train
        num_bytes: 3403699
        num_examples: 23516
    download_size: 1706476
    dataset_size: 3403699
  - config_name: Ade_corpus_v2_drug_ade_relation
    features:
      - name: text
        dtype: string
      - name: drug
        dtype: string
      - name: effect
        dtype: string
      - name: indexes
        struct:
          - name: drug
            sequence:
              - name: start_char
                dtype: int32
              - name: end_char
                dtype: int32
          - name: effect
            sequence:
              - name: start_char
                dtype: int32
              - name: end_char
                dtype: int32
    splits:
      - name: train
        num_bytes: 1545993
        num_examples: 6821
    download_size: 491362
    dataset_size: 1545993
  - config_name: Ade_corpus_v2_drug_dosage_relation
    features:
      - name: text
        dtype: string
      - name: drug
        dtype: string
      - name: dosage
        dtype: string
      - name: indexes
        struct:
          - name: drug
            sequence:
              - name: start_char
                dtype: int32
              - name: end_char
                dtype: int32
          - name: dosage
            sequence:
              - name: start_char
                dtype: int32
              - name: end_char
                dtype: int32
    splits:
      - name: train
        num_bytes: 64697
        num_examples: 279
    download_size: 33004
    dataset_size: 64697
configs:
  - config_name: Ade_corpus_v2_classification
    data_files:
      - split: train
        path: Ade_corpus_v2_classification/train-*
  - config_name: Ade_corpus_v2_drug_ade_relation
    data_files:
      - split: train
        path: Ade_corpus_v2_drug_ade_relation/train-*
  - config_name: Ade_corpus_v2_drug_dosage_relation
    data_files:
      - split: train
        path: Ade_corpus_v2_drug_dosage_relation/train-*
train-eval-index:
  - config: Ade_corpus_v2_classification
    task: text-classification
    task_id: multi_class_classification
    splits:
      train_split: train
    col_mapping:
      text: text
      label: target
    metrics:
      - type: accuracy
        name: Accuracy
      - type: f1
        name: F1 macro
        args:
          average: macro
      - type: f1
        name: F1 micro
        args:
          average: micro
      - type: f1
        name: F1 weighted
        args:
          average: weighted
      - type: precision
        name: Precision macro
        args:
          average: macro
      - type: precision
        name: Precision micro
        args:
          average: micro
      - type: precision
        name: Precision weighted
        args:
          average: weighted
      - type: recall
        name: Recall macro
        args:
          average: macro
      - type: recall
        name: Recall micro
        args:
          average: micro
      - type: recall
        name: Recall weighted
        args:
          average: weighted

Dataset Card for Adverse Drug Reaction Data v2

Table of Contents

Dataset Description

Dataset Summary

ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug. DRUG-AE.rel provides relations between drugs and adverse effects. DRUG-DOSE.rel provides relations between drugs and dosages. ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects.

Supported Tasks and Leaderboards

Sentiment classification, Relation Extraction

Languages

English

Dataset Structure

Data Instances

Config - Ade_corpus_v2_classification 

{
      'label': 1, 
      'text': 'Intravenous azithromycin-induced ototoxicity.'
}

Config - Ade_corpus_v2_drug_ade_relation 

{ 
    'drug': 'azithromycin', 
    'effect': 'ototoxicity', 
    'indexes': {
                  'drug': {
                            'end_char': [24], 
                            'start_char': [12]
                          }, 
                  'effect': {
                            'end_char': [44], 
                            'start_char': [33]
                            }
                }, 
    'text': 'Intravenous azithromycin-induced ototoxicity.'
    
}

Config - Ade_corpus_v2_drug_dosage_relation 

{
    'dosage': '4 times per day', 
    'drug': 'insulin', 
    'indexes': {
                'dosage': {
                            'end_char': [56], 
                            'start_char': [41]
                        }, 
                'drug': {
                          'end_char': [40], 
                          'start_char': [33]}
                        }, 
    'text': 'She continued to receive regular insulin 4 times per day over the following 3 years with only occasional hives.'
}

Data Fields

Config - Ade_corpus_v2_classification

  • text - Input text.
  • label - Whether the adverse drug effect(ADE) related (1) or not (0).

Config - Ade_corpus_v2_drug_ade_relation

  • text - Input text.
  • drug - Name of drug.
  • effect - Effect caused by the drug.
  • indexes.drug.start_char - Start index of drug string in text.
  • indexes.drug.end_char - End index of drug string in text.
  • indexes.effect.start_char - Start index of effect string in text.
  • indexes.effect.end_char - End index of effect string in text.

Config - Ade_corpus_v2_drug_dosage_relation

  • text - Input text.
  • drug - Name of drug.
  • dosage - Dosage of the drug.
  • indexes.drug.start_char - Start index of drug string in text.
  • indexes.drug.end_char - End index of drug string in text.
  • indexes.dosage.start_char - Start index of dosage string in text.
  • indexes.dosage.end_char - End index of dosage string in text.

Data Splits

Train
23516

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information 

@article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Language Processing in Pharmacogenomics",
issn = "1532-0464",
doi = "https://doi.org/10.1016/j.jbi.2012.04.008",
url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615",
author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo",
keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification",
abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus."
}

Contributions

Thanks to @Nilanshrajput, @lhoestq for adding this dataset.