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aisc-team-a1
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guidelines

Tasks:
Text Generation
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Text
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Languages:
English
Size:
10K - 100K
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Tags:
medical
health
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metadata
license: other
license_name: common-crawl
license_link: LICENSE
task_categories:
  - text-generation
language:
  - en
pretty_name: Clinical Guidelines
size_categories:
  - 10K<n<100K
tags:
  - medical
  - health
dataset_info:
  features:
    - name: id
      dtype: string
    - name: source
      dtype: string
    - name: title
      dtype: string
    - name: clean_text
      dtype: string
    - name: raw_text
      dtype: string
    - name: url
      dtype: string
    - name: overview
      dtype: string
  splits:
    - name: train
      num_bytes: 865223621
      num_examples: 37970
  download_size: 424262411
  dataset_size: 865223621
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

This is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/epfl-llm/guidelines

🎉 NEW DROP 🎉 PubMed Guidelines

We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas!

Clinical Guidelines

The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the Meditron Large Language Model (LLM). We publicly release a subset of 37K articles from our Guidelines corpus, extracted from 9 of 17 sources that allow content redistribution, namely CCO, CDC, CMA, ICRC, NICE, PubMed, SPOR, WHO and WikiDoc.

You can scrape and clean all 17 guideline sources using our code in epfLLM/meditron.

Sources of Clinical Practice Guidelines

Dataset Details

Dataset Creation

Curation Rationale

The dataset was curated to provide a high-quality collection of clinical practice guidelines (CPGs) for the medical training of LLMs. Our Clinical Guidelines corpus comprises 48,096 articles from 17 globally recognized sources for clinician and patient-directed guidance across high and low-resource settings, multiple medical domains (internal medicine, pediatrics, oncology, infectious disease, etc.) and multiple geographical locations.

Source Data

Clinical practice guidelines are rigorously researched frameworks designed to guide healthcare practitioners and patients in making evidence-based decisions regarding diagnosis, treatment, and management. They are compiled through a systematic process of collaborative consensus between experts to establish recommendations from the latest evidence on best practices that would maximize benefit in light of practical concerns such as available resources and context. As a super-synthesis of meta-analyses, they sit atop the evidence pyramid and form the basis of actionable evidence-based practice.

Clinical guidelines differ based on several factors:

  • Organizational level: CPGs are produced at various organizational granularities, ranging from global to hospital-level initiatives directed by international professional medical associations to informal consortia, regional or national governmental bodies to individual NGOs and hospitals.
  • Geographic scope: The geographic scope ranges from global (WHO) to national (CDC, NICE) and regional (Ontario, Melbourne) to institutional (ICRC, Mayo Clinic). This corpus is biased towards English-speaking regions due to its exclusive focus on English content.
  • Resource level: The corpus also represents health care concerns from high- (Ontario, Melbourne), low- (WHO), and volatile- (ICRC) resource settings.
  • Audience level: Guidelines also contains a range of technical and conversational vocabulary with target audiences of clinicians or patients (or both), and is sometimes highly specialized within a theme (cancer, pediatrics, infectious disease).
  • Peer-review: The peer review processes also ranged from UN bodies (WHO), institutional review boards (ICRC), professional associations (AAFP) to publicly crowdsourced knowledge bases (WikiDoc).
  • Document size: Article length varies widely from very short statements to 100+ page guides.

Who are the source data producers?

The dataset is sourced from 17 globally recognized medical entities, covering a wide range of healthcare contexts and audiences.

We employed pragmatic selection criteria over medical sources, seeking CPGs that were:

  • (1) open-access
  • (2) systematically formatted with homogenous textual structure (i.e., in a format in which automated processes could be deployed without excessive risk of misaligning textual sequences)
  • (3) in the language predominantly represented by the pre-training corpus of Llama (i.e., English)
  • (4) covering a breadth of medical sub-domains, audiences (clinician, nurse, patient), and resource settings (high, low, and humanitarian response settings)
Source Full Name Tag Guidelines Words Audience Country Released
AAFP American Academy of Family Physicians aafp 50 9.4K Doctor USA No
CCO Cancer Care Ontario cco 87 199K Doctor Canada Yes
CDC Center for Disease Control and Prevention cdc 621 6.7M Doctor USA Yes
CMA Canadian Medical Association cma 431 1.7M Doctor Canada Yes
CPS Canadian Paediatric Society cps 54 133K Doctor Canada No
drugs.com Drugs.com drugs 6548 4.1M Both International No
GuidelineCentral GuidelineCentral gc 1029 1M Doctor Mix No
ICRC International Committee of the Red Cross icrc 49 1.2M Doctor International Yes
IDSA Infectious Diseases Society of America idsa 47 646K Doctor USA No
MAGIC Making GRADE The Irresistible Choice magic 52 415K Doctor Mix No
MayoClinic MayoClinic mayo 1100 2.2M Patient USA No
NICE National Institute for Health and Care Excellence nice 1656 8.1M Doctor UK Yes
PubMed PubMed pubmed 1627 10.8M Doctor Mix Yes
RCH Royal Children's Hospital Melbourne rch 384 410K Doctor Australia No
SPOR Strategy for Patient-Oriented Research spor 217 1.1M Doctor Canada Yes
WHO World Health Organization who 223 3.1M Both International Yes
WikiDoc WikiDoc wikidoc 33058 34M Both International Yes

Data Collection and Processing

PDF documents were converted to text using GROBID. After extracting the raw text from each source, we cleaned data with an ad-hoc process to exclude irrelevant or repetitive content that did not contribute to the textual content, such as URLs, references, figures, table delimiters, and ill-formatted characters. This filtering procedure was performed differently for each source using a sample of 50 articles. Please note that this procedure is not perfect, as it may have removed useful information or kept superfluous content. We provide the raw_text for each article if you would like to perform your own cleaning step. Additionally, the text was standardized to a unified format with hierarchical section headers indicated by '#', homogenous spacing '\n\n' separating paragraphs, and normalized lists formatted with '- ' bullet points. Finally, all samples were deduplicated using title matching, and articles that were too short or not English were filtered out.

Personal and Sensitive Information

As the articles are publicly accessible, no personal or sensitive information is included.

Dataset Structure

Each row of the dataset represents one clinical practice guideline article, and consists of the following dataset fields (all strings):

Field Description Sources with field
id Unique identifier for each article All
source Source tag (cco, cdc, cma, icrc, nice, spor, who or wikidoc) All
title Title of the article CMA, NICE & WikiDoc
url URL of the article NICE, WikiDoc & PubMed
raw_text Unprocessed scraped article text All
clean_text Cleaned and formatted article text All
overview Short summary or abstract of the article NICE & Pubmed

Uses

The dataset is intended for use in tasks related to text generation, specifically in the context of clinical practice guidelines. It can be employed for training language models and other natural language processing applications within the healthcare domain.

Out-of-Scope Use

  • Redistribution: Please always check redistribution licenses before using the content as these may also evolve over time. To the best of our knowledge, we are following the redistribution licensing of each source and we invite users to inform us if that is not the case.
  • Malicious use: We do not support any use of this corpus that may be harmful. Creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. please do not create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice.

Bias, Risks, and Limitations

  • Peer-Review Quality: It is important to understand that while most sources are validated by internationally endorsed professional associations, a large proportion of articles are from Wikidoc which contains crowdsourced content. While edits in Wikidoc are generally restricted to expert review, the process of consensus and oversight is different from the traditional rigor of clinical guidelines.
  • Representation: This corpus is in English, and over-represents English-speaking regions. While we have included WHO and ICRC guidelines for low-resource settings, further work needs to be done to scrape sources from diverse contexts.
  • Temporal scope: Guidelines are constantly updated and these represent a snapshot of each in August 2023. Please re-scrape for updated content.

Recommendations

We warmly invite users to help us build a more representative corpus with high-quality peer-reviewed clinical practice guidelines in various languages and representing the full scope of clinical specialties and geographic regions. We encourage users of this content to be mindful of its current limitations in temporal and geographic scope and we repeat our warning: creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. Please don’t create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice.

Acknowledgments

The availability of open-access clinical practice guidelines (CPG) was critical to this work, and we thank all the societies listed above. A broader representation of geography, medical specialties, and contexts (especially low-resource settings) could be achieved through more standardized CPG formatting practices to ensure reliable textual extraction (e.g., releasing .txt or .html versions with structured content). We encourage the CPG community to continue to make these documents available (open-access with permissive licenses for incorporation into large language models) and easily usable.

Authors

  • Curation: Mary-Anne Hartley
  • Scraping: Antoine Bonnet, Alexandre Sallinen, Igor Krawczuk, Kyle Matoba
  • Cleaning: Antoine Bonnet, Alexandre Sallinen

Citation

If you use the Clinical Guidelines corpus, please cite out work:

@misc{chen2023meditron70b,
      title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models},
      author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
      year={2023},
      eprint={2311.16079},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@software{epfmedtrn,
  author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
  title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},
  month = November,
  year = 2023,
  url = {https://github.com/epfLLM/meditron}
}