---
dataset_info:
- config_name: conversational_medical
  features:
  - name: id
    dtype: int64
  - name: prompt
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: completion
    list:
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      dtype: string
    - name: content
      dtype: string
  - name: Label
    dtype: string
  splits:
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    num_examples: 4904
  - name: dev
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  - name: test
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  download_size: 6339380
  dataset_size: 17497505
- config_name: conversational_patient
  features:
  - name: id
    dtype: int64
  - name: prompt
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: completion
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: Label
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  - name: dev
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  download_size: 8521486
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- config_name: processed-medical
  features:
  - name: id
    dtype: int64
  - name: topic_id
    dtype: int64
  - name: Description_Medical-Language
    dtype: string
  - name: CTR_Context
    dtype: string
  - name: CTR_Title
    dtype: string
  - name: CTR_id
    dtype: string
  - name: Label
    dtype: string
  splits:
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    num_examples: 4904
  - name: dev
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    num_examples: 525
  - name: test
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  download_size: 6690961
  dataset_size: 18380245
- config_name: processed-patient
  features:
  - name: id
    dtype: int64
  - name: topic_id
    dtype: int64
  - name: Description_Patient-Language
    dtype: string
  - name: CTR_Context
    dtype: string
  - name: CTR_Title
    dtype: string
  - name: CTR_id
    dtype: string
  - name: Label
    dtype: string
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    num_examples: 4904
  - name: dev
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    num_examples: 525
  - name: test
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  download_size: 6696616
  dataset_size: 18338267
- config_name: source
  features:
  - name: id
    dtype: int64
  - name: topic_id
    dtype: int64
  - name: statement_medical
    dtype: string
  - name: statement_pol
    dtype: string
  - name: premise
    dtype: string
  - name: NCT_title
    dtype: string
  - name: NCT_id
    dtype: string
  - name: label
    dtype: string
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  - name: dev
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  - name: test
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  download_size: 6841482
  dataset_size: 22587309
configs:
- config_name: conversational_medical
  data_files:
  - split: train
    path: conversational_medical/train-*
  - split: dev
    path: conversational_medical/dev-*
  - split: test
    path: conversational_medical/test-*
- config_name: conversational_patient
  data_files:
  - split: train
    path: conversational_patient/train-*
  - split: dev
    path: conversational_patient/dev-*
  - split: test
    path: conversational_patient/test-*
- config_name: processed-medical
  data_files:
  - split: train
    path: processed-medical/train-*
  - split: dev
    path: processed-medical/dev-*
  - split: test
    path: processed-medical/test-*
- config_name: processed-patient
  data_files:
  - split: train
    path: processed-patient/train-*
  - split: dev
    path: processed-patient/dev-*
  - split: test
    path: processed-patient/test-*
- config_name: source
  data_files:
  - split: train
    path: source/train-*
  - split: dev
    path: source/dev-*
  - split: test
    path: source/test-*
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- medical
pretty_name: NLI4PR
size_categories:
- 10K<n<100K
---


# Natural Language Inference for Patient Recruitment (NLI4PR)

## Dataset Description

|                                 | Links         | 
|:-------------------------------:|:-------------:|
| **Homepage:**                   |  [Github.io](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)  | 
| **Repository:**                 |  [Github](https://aclanthology.org/2025.cl4health-1.21/)  | 
| **Paper:**                      |  [arXiv](https://arxiv.org/abs/2503.15718)  | 
| **Contact (Original Authors):** |  Mathilde Aguiar (mathilde.aguiar@lisn.fr) |
| **Contact (Curator):**          |  [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com) |

  
### Dataset Summary

`MedQA is a large-scale multiple-choice question-answering dataset designed to mimic the style of professional medical board exams, particularly the USMLE (United States Medical Licensing Examination). Introduced by Jin et al. in 2020 under the title “What Disease Does This Patient Have? A Large‑scale Open‑Domain Question Answering Dataset from Medical Exams”, the dataset supports open-domain QA via retrieval from medical textbooks`

### Data Instances

#### Source Format

```json
{
        "id": "5088",
        "topic_id": "39",
        "statement_medical": "A 55-year-old white woman comes for a routine checkup.  She has no significant medical history and does not use tobacco, alcohol, or illicit drugs.  The patient's only medication is an over-the-counter multivitamin.  Family history is notable for a hip fracture in her mother.  Blood pressure is 130\/80 mm Hg and pulse is 112\/min. She has occasional back pain and lives a sedentary lifestyle with the BMI of 24 Kg\/m2. Plain X-ray of the spine shows mild compression fracture at the level of T10. X-ray absorptiometry studies demonstrate abnormally low bone density in the lumbar vertebrae and T-score values below -2.5, which confirms the diagnosis of osteoporosis.",
        "statement_pol": "I'm a 55-year-old white woman and I recently visited my family doctor. I don't smoke anything or drink. I don't have any remarkable medical history. I only use over-the-counter multivitamins to keep myself fresh and energized. My mom had a hip fracture. The doctor took my blood pressure and it was 130\/80 and my pulse was 112\/min. I have annoying back pain from time to time and to be honest I don't exercise much or move much. My BMI is 24. I did a spine X-ray a while ago and my doctor showed me that I have a fracture on one of my vertebrae. I also have a low bone density in my lumbar vertebrae and T-score values below -2.5. The doctor diagnosed me with osteoporosis.",
        "premise": "Inclusion Criteria:\n\n          -  Postmenopausal women and men referred for bone density examination.\n\n        Exclusion Criteria:\n\n          -  Patients unable to sign consent for participation.\nNo condition on gender to be admitted to the trial.\nAccepts Healthy Volunteers\nSubject must be at least 20 Years old.\nSubject must be at most 90 Years",
        "NCT_title": "Bindex Ultrasonometer for Osteoporosis Diagnostics",
        "NCT_id": "NCT01935232",
        "label": "Contradiction"
}
```

### Data Fields

#### Source Format

TO:DO

### Data Splits

TO:DO

## Additional Information

### Dataset Curators

#### Original Paper

- Mathilde Aguiar (mathilde.aguiar@lisn.fr) - Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique
- Pierre Zweigenbaum - Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique
- Nona Naderi - Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique

#### Huggingface Curator

- [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com) - INESC-ID / University of Lisbon - Instituto Superior Técnico

### Licensing Information

[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)

### Citation Information

```bibtex
  @misc{aguiar2025ieligiblenaturallanguage,
        title={Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View}, 
        author={Mathilde Aguiar and Pierre Zweigenbaum and Nona Naderi},
        year={2025},
        eprint={2503.15718},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2503.15718}, 
  }
```

[10.18653/v1/2025.cl4health-1.21](http://doi.org/10.18653/v1/2025.cl4health-1.21)

### Contributions

Thanks to [araag2](https://github.com/araag2) for adding this dataset.