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

Dataset Card for Clinical Guidelines corpus

The Clinical Guidelines corpus is a new dataset of 46K clinical practice guidelines from various medical sources. This dataset is part of the original training corpus of the Meditron LLM.

We release 35K scraped articles (in both raw and cleaned form) from 8 of 16 clinical guidelines sources allowing redistribution (namely CCO, CDC, CMA, ICRC, NICE, SPOR, WHO & WikiDoc). You can scrape and clean all 16 guideline sources using our web scrapers and pre-processing code in epfLLM/meditron.

Source	Full Name	Source tag	Total guidelines	Total words	Audience	Released
AAFP	American Academy of Family Physicians	`aafp`	50	9.4K	Doctor	No
CCO	Cancer Care Ontario	`cco`	87	199K	Doctor	Yes
CDC	Center for Disease Control and Prevention	`cdc`	621	6.7M	Doctor	Yes
CMA	Canadian Medical Association	`cma`	431	1.7M	Doctor	Yes
CPS	Canadian Paediatric Society	`cps`	54	133K	Doctor	No
drugs.com	Drugs.com	`drugs`	6548	4.1M	Both	No
GuidelineCentral	GuidelineCentral	`gc`	1029	1M	Doctor	No
ICRC	International Committee of the Red Cross	`icrc`	49	1.2M	Doctor	Yes
IDSA	Infectious Diseases Society of America	`idsa`	47	646K	Doctor	No
MAGIC	Making GRADE The Irresistible Choice	`magic`	52	415K	Doctor	No
MayoClinic	MayoClinic	`mayo`	1100	2.2M	Patient	No
NICE	National Institute for Health and Care Excellence	`nice`	1656	8.1M	Doctor	Yes
RCH	Royal Children's Hospital Melbourne	`rch`	384	410K	Doctor	No
SPOR	Strategy for Patient-Oriented Research	`spor`	217	1.1M	Doctor	Yes
WHO	World Health Organization	`who`	223	3.1M	Both	Yes
WikiDoc	WikiDoc	`wikidoc`	33058	34M	Both	Yes

Dataset Details

Dataset Description

Curated by: EPFL LLM Team
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): English only
License: [More Information Needed]

Dataset Sources [optional]

Repository: epfLLM/meditron
Paper [optional]: MediTron-70B: Scaling Medical Pretraining for Large Language Models
Demo [optional]: [More Information Needed]

Uses

Direct Use

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Out-of-Scope Use

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Dataset Structure

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

id: Unique identifier for each article.
source: Source tag ('cco', 'cdc', 'cma', 'icrc', 'nice', 'spor', 'who' or 'wikidoc')
title: Title of the article (only for CMA, NICE and WikiDoc)
url: URL of the article (only for NICE and WikiDoc)
raw_text: Unprocessed scraped article text
clean_text: Cleaned and formatted article text
overview: Short summary of the article (only for NICE)

Dataset Creation

Curation Rationale

Clinical practice guidelines (CPGs) 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. CPGs are produced at various geographic and 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. Our Clinical Guidelines corpus comprises $46,469$ guideline articles from 16 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 various geographic granularities.

Source Data

The \guidelines corpus comprises a broad range of contexts. For instance, the geographic scope ranges from global (WHO) to national (CDC, NICE) and regional (Ontario, Melbourne) to institutional (ICRC, Mayo Clinic). The corpus also represents health care concerns from high- (Ontario, Melbourne), low- (WHO), and volatile- (ICRC) resource settings. \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). The peer review processes also ranged from UN bodies (WHO), institutional review boards (ICRC), professional associations (AAFP) to publicly crowdsourced knowledge bases (WikiDoc).

Data Collection and Processing

After extracting the raw text from each source, we cleaned data 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. Additionally, the text was standardized to a unified format with indicated section headers, homogenous space separating paragraphs, and normalized lists. Finally, all samples were deduplicated using title matching, and articles that were too short or not English were filtered out.

Who are the source data producers?

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).

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Annotations [optional]

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

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