---
language:
- en
- zh
- tw
license: mit
tags:
- text
- question-and-answer
pretty_name: MedQA
task_categories:
- question-answering
configs:
- config_name: en
  data_files:
  - split: train
    path: data/questions/en/train.jsonl
  - split: dev
    path: data/questions/en/dev.jsonl
  - split: test
    path: data/questions/en/test.jsonl
  - split: all_splits
    path: data/questions/en/all_splits.jsonl
- config_name: tw
  data_files:
  - split: train
    path: data/questions/tw/train.jsonl
  - split: dev
    path: data/questions/tw/dev.jsonl
  - split: test
    path: data/questions/tw/test.jsonl
  - split: all_splits
    path: data/questions/tw/all_splits.jsonl
- config_name: zh
  data_files:
  - split: train
    path: data/questions/zh/train.jsonl
  - split: dev
    path: data/questions/zh/dev.jsonl
  - split: test
    path: data/questions/zh/test.jsonl
  - split: all_splits
    path: data/questions/zh/all_splits.jsonl
- config_name: xlang
  data_files:
  - split: train
    path: data/questions/xlang/train.jsonl
  - split: dev
    path: data/questions/xlang/dev.jsonl
  - split: test
    path: data/questions/xlang/test.jsonl
  - split: all_splits
    path: data/questions/xlang/all_splits.jsonl
- config_name: en_5
  data_files:
  - split: train
    path: data/questions/en_5/train.jsonl
  - split: dev
    path: data/questions/en_5/dev.jsonl
  - split: test
    path: data/questions/en_5/test.jsonl
  - split: all_splits
    path: data/questions/en_5/all_splits.jsonl
- config_name: zh_5
  data_files:
  - split: train
    path: data/questions/zh_5/train.jsonl
  - split: dev
    path: data/questions/zh_5/dev.jsonl
  - split: test
    path: data/questions/zh_5/test.jsonl
  - split: all_splits
    path: data/questions/zh_5/all_splits.jsonl
---

# Dataset Card for MedQA

- **Homepage:** [https://github.com/jind11/MedQA](https://github.com/jind11/MedQA)
  - This is an unofficial curation of the MedQA dataset, uploaded here with minimal (i.e., no content-modifying) processing.
- **Paper:** [*What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams*](https://www.mdpi.com/2076-3417/11/14/6421) (MDPI)
- **Languages:** English (en), Taiwanese (tw), and Chinese (zh).


## Dataset Subsets

This dataset contains multiple configs:

- QA with four possible answers (as reported in the paper)
  - `en`: English instances
  - `tw`: Taiwanese instances
  - `zh`: Chinese instances
  - `xlang`: instances in any language
- QA with five possible answers (the original datasets for English and Chinese)
  - `en_5`
  - `zh_5`

Data can be loaded by specifying the config and data split:

```
from datasets import load_dataset

data = load_dataset("mathewhe/medqa", "en", split="train")
```

Possible splits are "train", "dev", and "test".

## Dataset Structure

Each data subset will contain the following columns:

```
question (string): The question/prompt.
answer: The correct response.
answer_idx: The multiple-choice identifier for the correct response.
A: The "A" answer.
B: The "B" answer.
C: The "C" answer.
D: The "D" answer.
E (in `en_5` or `zh_5` subsets): The "E" answer.
language: "en", "tw", or "zh".
```

Example from en-train:

| question                | answer         | meta_info | answer_idx | A          | B           | C           | D              | language |
|-------------------------|----------------|-----------|------------|------------|-------------|-------------|----------------|----------|
| A 23-year-old pregna... | Nitrofurantoin | step2&3   | D          | Ampicillin | Ceftriaxone | Doxycycline | Nitrofurantoin | en       |



## Citation Information

For reproducibility, please include a link to *this* dataset when publishing results based on the included data.

For formal citations, please cite the *original* publication:
```
@article{jin2020disease,
  title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
  author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
  journal={arXiv preprint arXiv:2009.13081},
  year={2020}
}
```