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Datasets:
laiking
/
prim-sec-outcomes

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
biomedical
clinical-trial
scientific-articles
Libraries:
Datasets
pandas
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metadata
dataset_info:
  features:
    - name: id
      dtype: string
    - name: tokens
      sequence: string
    - name: labels
      sequence:
        class_label:
          names:
            '0': '0'
            '1': B-PrimaryOutcome
            '2': I-PrimaryOutcome
            '3': B-SecondaryOutcome
            '4': I-SecondaryOutcome
  splits:
    - name: train
      num_bytes: 1740905
      num_examples: 3660
    - name: test
      num_bytes: 277244
      num_examples: 564
  download_size: 423457
  dataset_size: 2018149
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
task_categories:
  - token-classification
language:
  - en
tags:
  - biomedical
  - clinical-trial
  - scientific-articles
size_categories:
  - 1K<n<10K

Dataset Card for Dataset Name

Corpus for token classification of primary and secondary outcomes in scientific articles sentences, in BIO format.

Dataset Details

Dataset Description

Filtered the EBM-NLP corpus Outcomes subset and did the following processing:

  • split examples into sentences and get the entities for each sentence
  • verify in each sentences mentions of primary and secondary outcomes using regular expression (with synonyms)
  • tagged all outcomes according to type when the regex was found in a sentence
  • removed entity tags for all sentences that were not detected as containing primary or secondary outcome
  • kept the examples at sentence level
  • kept original train/test set

Then added data from A. koroleva on primary outcomes (manually annotated corpus). Part of this data is

Then added manually annotated data from a study on primary outcome switching in colorectal cancer articles (this data is only used in test set).

Finally as the filtered EBM-NLP contained a lot of sequences without entities in the end, we sampled from these sequences so that we have an equal number of sequences with and without entities, in train and test set.

  • Curated by: Mathieu Laï-king
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Language(s) (NLP): English
  • License: None

Dataset Sources [optional]

From 2 existing corpus one from A. Koroleva and the other is EBM-NLP.

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
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Uses

Direct Use

Fine tuning NER models

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

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Annotation process

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

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