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--- |
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annotations_creators: |
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- no-annotation |
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language: |
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- en |
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language_creators: |
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- found |
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- other |
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license: |
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- mit |
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multilinguality: |
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- monolingual |
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pretty_name: MedQA Textbook (English) Corpus |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- med_qa |
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tags: |
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- medical |
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- clinical medicine |
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- biology |
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task_categories: |
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- text-generation |
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task_ids: |
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- language-modeling |
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--- |
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# Dataset Card for MedQA English Textbooks |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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### Dataset Summary |
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[MedQA](https://github.com/jind11/MedQA) includes |
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> 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]. |
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This dataset is derived from this medical textbook content (those in English), providing subsets that coincide with Medical |
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subspecialties for use in pre-training medical LLMs with gold standard domain text. |
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### Languages |
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English |
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## Dataset Structure |
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### Data Instances |
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Records have the following structure |
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```json |
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{"text": "The manifestations of acute intestinal obstruction depend on the nature of the underlying [..]", |
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"source": "textbooks/en/InternalMed_Harrison.txt"} |
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``` |
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## Dataset Creation |
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### Curation Rationale |
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The MedQA dataset includes raw text corpus that is excluded from most of its [derivations](https://huggingface.co/datasets/bigbio/med_qa) |
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and their [dataset loading scripts](https://huggingface.co/datasets/bigbio/med_qa/blob/main/med_qa.py) . This raw text is valuable for pre-training of medical LLMS. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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Langchain's RecursiveCharacterTextSplitter is used for chunking and the most commonly-appearing non-ASCII characters |
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are replaced with readable equivalents. Chunks comprising less than 90% ASCII characters were excluded. The textbooks |
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were then broken into separate subsets, indicated below along with the textbook source(s) they comprise: |
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- Core Clinical Medicine (_*core_clinical*_) |
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- 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) |
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- Basic Biology (_*basic_biology*_) |
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- 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) |
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- Pharmacology (_*pharmacology*_) |
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- Pharmacology_Katzung.txt (4,505 records) |
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- Psychiatry (_*psychiatry*_) |
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- Psichiatry_DSM-5.txt (2,414 records) |
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So, you can load the basic biology subset of the corpus via: |
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```python |
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In [1]: import datasets |
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In [2]: ds = datasets.load_dataset('cogbuji/medqa_corpus_en', 'basic_biology') |
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Generating train split: 50386 examples [00:00, 92862.56 examples/s] |
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In [3]: ds |
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Out[3]: |
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DatasetDict({ |
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train: Dataset({ |
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features: ['text', 'source'], |
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num_rows: 50386 |
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}) |
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}) |
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``` |
