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--- |
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license: mit |
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size_categories: |
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- n<1K |
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task_categories: |
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- text-classification |
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- token-classification |
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pretty_name: FDA CDRH Device Recalls NER Dataset |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: ner_tags |
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sequence: int64 |
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- name: tokens |
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sequence: string |
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- name: labels |
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sequence: string |
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splits: |
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- name: train |
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num_bytes: 3157376.00498008 |
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num_examples: 1606 |
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- name: test |
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num_bytes: 790326.9950199203 |
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num_examples: 402 |
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download_size: 522272 |
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dataset_size: 3947703.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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tags: |
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- medical |
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--- |
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# Dataset Card for FDA CDRH Device Recalls NER Dataset |
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This is a FDA Medical Device Recalls Dataset Created for Medical Device Named Entity Recognition (NER) |
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## Dataset Details |
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### Dataset Description |
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This dataset was created for the purpose of performing NER tasks. |
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It utilizes the OpenFDA Device Recalls dataset, which has been processed and annotated for performing NER. |
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The Device Recalls dataset has been further processed to extract the recall action element, which is utilized for annotation in this dataset. |
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- **Curated by:** Miriam Farrington for CS224N - Natural Language Processing with Deep Learning |
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- **Language(s) (NLP):** Python, TensorFlow |
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- **License:** MIT |
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### Dataset Sources [optional] |
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<!-- Provide the basic links for the dataset. --> |
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- **Source:** https://open.fda.gov/apis/device/recall/ |
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- **Tools** https://doccano.github.io/doccano/ |
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- **Paper [optional]:** Link TBD |
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- **Repository:** Link TBD |
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## Uses |
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### Direct Use |
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This dataset can be used to finetune a pretrained model such as BERT or BioBERT to identify and label medical device trade names, product codes and device components. |
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## Dataset Structure |
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This dataset contains the following fields: |
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id - unique device recall identifier |
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text - text of the device recall action |
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label - NER label (B-DEVICE, I-DEVICE, O-DEVICE) |
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## Dataset Creation |
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#### Data Collection and Processing |
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The data was collected and preprocessed to extract the action element from the Recalls dataset. |
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It was cleaned, de-duplicated and annotated in the B-I-O format for NER using the Doccano open-source annotation tool. |
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The NER annotations (O-DEVICE, I-DEVICE, B-DEVICE) are mapped to corresponding tag values (0,1,2) |
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BioBERT tokenization is performed on the inputs and labels are realigned following tokenization |
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### Annotations [optional] |
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#### Annotation process |
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A subset of the source data which represents this dataset was annotated utilizing the Doccano open-source annotation tool. |
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The annotation process follows this methodology when applying Device NER labels: |
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1. Device Tradenames are annotated with the NER labels |
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2. Device names which represent more than 1 word are annotated using the B-DEVICE, I-DEVICE format. |
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3. Words which are part of the device name, but should be excluded from the dataset are labeled O-DEVICE |
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4. Remaining words are not labeled. |
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It should be noted that, while both "O" and unlabeled words might not be entities, |
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the "O" label explicitly indicates that a word has been evaluated and deemed not to be an entity, whereas unlabeled words |
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haven't been evaluated or are not relevant for NER. |
