README.md

---
annotations_creators:
- train: programmatically-generated
- val: programmatically-generated
- test: programmatically-generated, expert-validated
language_creators:
- found
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K **FIX**
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
---
## 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
- **Repository:** [Github](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard)
- **Paper:** Pending

### Dataset Summary

EpiSet4NER is a bronze-standard dataset for epidemiological entity recognition of location, epidemiologic types, and  created using weakly-supervised teaching methods

locations, epidemiological identifiers (e.g. "prevalence", "annual incidence", "estimated occurrence") and epidemiological rates (e.g. "1.7 per 1,000,000 live births", "2.1:1.000.000", "one in five million", "0.03%")

These are the V3 training (456 abstracts), validation (114 abstracts), and programmatically generated test (50 abstracts) set. The training set was copied to ```datasets/EpiCustomV3``` and renamed *train.tsv*. The validation set was copied to ```datasets/EpiCustomV3``` and ```datasets/Large_DatasetV3``` and renamed *val.tsv*. The V3 test set (uncorrected) is important as it is used by *Find efficacy of test predictions.ipynb* to find the efficacy of the programmatic labeling, but was otherwise not used with the model.
[NIH NCATS GARD](https://rarediseases.info.nih.gov/)

An example of 'train' looks as follows.
```
{
    "id": "333",
    "tokens": ['Conclusions', 'The', 'birth', 'prevalence', 'of', 'CLD', 'in', 'the', 'northern', 'Netherlands', 'was', '21.1/10,000', 'births', '.'],
    "ner_tags":  [0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 5, 6, 0],
}
```
### Data Fields

The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-EPI` (3), `I-EPI` (4),`B-STAT` (5),`I-STAT` (6).

### Data Splits by number of tokens

|name |train |validation|test|
|---------|-----:|----:|----:|
|EpiSet   |117888|31262|13910|

## Dataset Creation

This bronze-standard dataset was created from 620 rare disease abstracts
Programmatic Labeling using statistical and rule-based methods (Weakly Supervised Teaching)
![Text Labeling](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/Text%20Labeling4.png)

| Evaluation Level |          Entity          | Precision | Recall |   F1  |
|:----------------:|:------------------------:|:---------:|:------:|:-----:|
|   Entity-Level   |          Overall         |   0.559   |  0.662 | 0.606 |
|                  |         Location         |   0.597   |  0.661 | 0.627 |
|                  | Epidemiologic Identifier |   0.854   |  0.911 | 0.882 |
|                  |    Epidemiologic Rate    |   0.175   |  0.255 | 0.207 |
|------------------|--------------------------|-----------|--------|-------|
|    Token-Level   |          Overall         |   0.805   |  0.710 | 0.755 |
|                  |         Location         |   0.868   |  0.713 | 0.783 |
|                  |    Epidemiologic Type    |   0.908   |  0.908 | 0.908 |
|                  |    Epidemiologic Rate    |   0.739   |  0.645 | 0.689 |

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

A sample of 500 disease names were gathered from ~6061 rare diseases tracked by GARD.

### Annotations

#### Annotation process

See here and then here

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

Generates *whole_abstract_set.csv* and *positive_abstract_set.csv*. *whole_abstract_set.csv* is a dataset created by sampling 500 rare disease names and their synonyms from *GARD.csv* until &ge;50 abstracts had been returned or the search results were exhausted. Although ~25,000 abstracts were expected, 7699 unique abstracts were returned due to the limited research on rare diseases. After running each of these through the LSTM RNN classifier, the *positive_abstract_set.csv* was created from the abstracts which had an epidemiological probability >50%. *positive_abstract_set.csv* will be passed to *create_labeled_dataset_V2.ipynb*


### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions
Thanks to [@William Kariampuzha](https://github.com/wzkariampuzha) at NCATS/Axle Informatics for adding this dataset.