---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: MedQA Textbook (English) Corpus
size_categories:
- 10K<n<100K
source_datasets:
- extended|medmcqa
tags:
- medical
- clinical medicine
- biology
task_categories:
- text-generation
task_ids:
- language-modeling
---

# Dataset Card for MedQA English Textbooks

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
- [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)

![image/png](https://huggingface.co/datasets/cogbuji/medqa_corpus_en/resolve/main/shelves.png?download=true)

## Dataset Description

### Dataset Summary

[MedQA](https://github.com/jind11/MedQA) includes
> prepared text materials from a total of 18 English medical textbooks that have been widely used by medical students and USMLE takers" [Jin, Di, et al. 2020].

This dataset is derived from these medical textbooks (those in English), providing subsets that coincide with Medical 
subspecialties for use in pre-training medical LLMs with gold standard domain text.

### Languages

English

## Dataset Structure

### Data Instances

Records have the following structure

```json
{"text": "The manifestations of acute intestinal obstruction depend on the nature of the underlying [..]", 
 "source": "textbooks/en/InternalMed_Harrison.txt"}
```
## Dataset Creation

### Curation Rationale

The MedQA dataset includes raw text corpus that is excluded from most of its derivations and the raw text is 
valuable for pre-training of medical LLMS.

### Source Data

#### Initial Data Collection and Normalization

Langchain's RecursiveCharacterTextSplitter is used for chunking and the most commonly-appearing non-ASCII characters 
are replaced with readable equivalents.   The textbooks are then broken into separate subsets, indicated below along with 
the textbooks they comprise: 

- Core Clinical Medicine (_*core_clinical*_)
  - Anatomy_Gray.txt, First_Aid_Step1.txt, First_Aid_Step2.txt, Immunology_Janeway.txt, InternalMed_Harrison.txt, Neurology_Adams.txt, Obstentrics_Williams.txt, Pathoma_Husain.txt, Pediatrics_Nelson.txt, and Surgery_Schwartz.txt 
- Basic Biology (_*basic_biology*_)
  - Biochemistry_Lippincott.txt, Cell_Biology_Alberts.txt, Histology_Ross.txt, Pathology_Robbins.txt, and Physiology_Levy.txt 
- Pharmacology (_*pharmacology*_)
  - Pharmacology_Katzung.txt 
- Psychiatry (_*psychiatry*_)
  - Psichiatry_DSM-5.txt

So, you can load the basic biology subset of the corpus via:

```python
In [1]: import datasets
In [2]: ds = datasets.load_dataset('cogbuji/medqa_corpus_en', 'basic_biology')
Generating train split: 50386 examples [00:00, 92862.56 examples/s]
In [3]: ds 
Out[3]: 
DatasetDict({
    train: Dataset({
        features: ['text', 'source'],
        num_rows: 50386
    })
})
```