task_categories:text-classification task_categories:token-classification task_ids:coreference-resolution task_ids:fact-checking annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:10K<n<100K size_categories:1K<n<10K size_categories:n<1K source_datasets:original language:en license:unknown region:us
 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.
@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."
}
---
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
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: 3403711
    num_examples: 23516
  download_size: 3791162
  dataset_size: 3403711
- 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: 1546021
    num_examples: 6821
  download_size: 3791162
  dataset_size: 1546021
- 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: 64725
    num_examples: 279
  download_size: 3791162
  dataset_size: 64725
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
config_names:
- Ade_corpus_v2_classification
- Ade_corpus_v2_drug_ade_relation
- Ade_corpus_v2_drug_dosage_relation
---

# Dataset Card for Adverse Drug Reaction Data v2

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://www.sciencedirect.com/science/article/pii/S1532046412000615
- **Repository:** [Needs More Information]
- **Paper:** https://www.sciencedirect.com/science/article/pii/S1532046412000615
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### 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](https://github.com/Nilanshrajput), [@lhoestq](https://github.com/lhoestq) for adding this dataset.