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Dataset Card for MedQA English Textbooks
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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