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Datasets:
cogbuji
/
medqa_corpus_en

Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
other
Annotations Creators:
no-annotation
Source Datasets:
med_qa
Tags:
medical
clinical medicine
biology
License:
Dataset card Files Community
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The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

Dataset Card for MedQA English Textbooks

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Dataset Description

Dataset Summary

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 this medical textbook content (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

{"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 their dataset loading scripts . This 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. Chunks comprising less than 90% ASCII characters were excluded. The textbooks were then broken into separate subsets, indicated below along with the textbook source(s) they comprise:

  • Core Clinical Medicine (core_clinical)
    • Anatomy_Gray.txt (1,736 records), First_Aid_Step1.txt (489 records), First_Aid_Step2.txt (800 records), Immunology_Janeway.txt (2,996 records), InternalMed_Harrison.txt (20,583 records), Neurology_Adams.txt (7,732 records), Obstentrics_Williams.txt (5,392 records), Pathoma_Husain.txt (280 records), Pediatrics_Nelson.txt (2,575 records), and Surgery_Schwartz.txt (7,803 records)
  • Basic Biology (basic_biology)
    • Biochemistry_Lippincott.txt (1,193 records), Cell_Biology_Alberts.txt (4,275 records), Histology_Ross.txt (2,685 records), Pathology_Robbins.txt (3,156 records), and Physiology_Levy.txt (2,627 records)
  • Pharmacology (pharmacology)
    • Pharmacology_Katzung.txt (4,505 records)
  • Psychiatry (psychiatry)
    • Psichiatry_DSM-5.txt (2,414 records)

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

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
    })
})
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