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--- |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- medical |
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- question answering |
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- large language model |
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- retrieval-augmented generation |
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size_categories: |
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- 100K<n<1M |
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--- |
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# The StatPearls Corpus in MedRAG |
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This HF dataset contains the chunked snippets from the StatPearls corpus used in [MedRAG](https://arxiv.org/abs/2402.13178). It can be used for medical Retrieval-Augmented Generation (RAG). |
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According to the [privacy policy](https://www.statpearls.com/home/privacypolicy/) of StatPearls, **we are not allowed to distribute the StatPearls content**. Please download the raw data from [NCBI Bookshelf](https://www.ncbi.nlm.nih.gov/books/NBK430685/) and use [our code](https://github.com/Teddy-XiongGZ/MedRAG/blob/main/src/data/statpearls.py) for the chunking. |
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## Dataset Details |
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### Dataset Descriptions |
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[StatPearls](https://www.statpearls.com/) is a point-of-the-care clinical decision support tool similar to [UpToDate](https://www.uptodate.com/). We use the 9,330 publicly available StatPearl articles through [NCBI Bookshelf](https://www.ncbi.nlm.nih.gov/books/NBK430685/) to construct the StatPearls corpus. |
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We chunked StatPearls according to the hierarchical structure, treating each paragraph in an article as a snippet and splicing all the relevant hierarchical headings as the corresponding title. |
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Our chunked corpus contains 301,202 snippets with an average of 119 tokens. |
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### Dataset Structure |
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Each row is a snippet of StatPearls, which includes the following features: |
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- id: a unique identifier of the snippet |
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- title: the title and subtitles of the StatPearl article from which the snippet is collected |
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- content: the content of the snippet |
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- contents: a concatenation of 'title' and 'content', which will be used by the [BM25](https://github.com/castorini/pyserini) retriever |
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## Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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### Direct Use |
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<!-- This section describes suitable use cases for the dataset. --> |
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```shell |
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git clone https://github.com/Teddy-XiongGZ/MedRAG.git |
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cd MedRAG |
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wget https://ftp.ncbi.nlm.nih.gov/pub/litarch/3d/12/statpearls_NBK430685.tar.gz -P ./corpus/statpearls |
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tar -xzvf ./corpus/statpearls/statpearls_NBK430685.tar.gz -C ./corpus/statpearls |
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python src/data/statpearls.py |
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``` |
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### Use in MedRAG |
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```python |
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>> from src.medrag import MedRAG |
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>> question = "A lesion causing compression of the facial nerve at the stylomastoid foramen will cause ipsilateral" |
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>> options = { |
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"A": "paralysis of the facial muscles.", |
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"B": "paralysis of the facial muscles and loss of taste.", |
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"C": "paralysis of the facial muscles, loss of taste and lacrimation.", |
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"D": "paralysis of the facial muscles, loss of taste, lacrimation and decreased salivation." |
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} |
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>> medrag = MedRAG(llm_name="OpenAI/gpt-3.5-turbo-16k", rag=True, retriever_name="MedCPT", corpus_name="StatPearls") |
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>> answer, snippets, scores = medrag.answer(question=question, options=options, k=32) # scores are given by the retrieval system |
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``` |
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## Citation |
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```shell |
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@article{xiong2024benchmarking, |
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title={Benchmarking Retrieval-Augmented Generation for Medicine}, |
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author={Guangzhi Xiong and Qiao Jin and Zhiyong Lu and Aidong Zhang}, |
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journal={arXiv preprint arXiv:2402.13178}, |
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year={2024} |
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} |
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``` |
