Update README.md

README.md

@@ -42,41 +42,16 @@ You can scrape and clean all 16 guideline sources using our code in [epfLLM/medi
 
 ## Dataset Details
 
-### Dataset Description
-
 <!-- Provide a longer summary of what this dataset is. -->
 
 - **Curated by:** [EPFL LLM Team](https://huggingface.co/epfl-llm)
 - **Funded by:** [More Information Needed]
 - **Language(s):** English only
 - **License:** [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use)
-- **Knowledge Cutoff**: August 2023
-
-### Dataset Sources
-
-<!-- Provide the basic links for the dataset. -->
-
 - **Repository:** [epfLLM/meditron](https://github.com/epfLLM/meditron)
 - **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models]()*
+- **Knowledge Cutoff**: August 2023
 
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-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
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-- **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 don’t create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice.
-
-[More Information Needed]
 
 ## Dataset Structure
 
@@ -95,7 +70,6 @@ Each row of the dataset represents one clinical practice guideline article, and
 | `clean_text`| Cleaned and formatted article text        | All                          |
 | `overview`  | Short summary of the article               | NICE only                     |
 
-
 ## Dataset Creation
 
 ### Curation Rationale
@@ -103,21 +77,32 @@ Each row of the dataset represents one clinical practice guideline article, and
 <!-- Motivation for the creation of this dataset. -->
 
 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 46,469 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 multiple geographical locations. 
- 
-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. 
-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. 
-
 
 ### Source Data
 
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 
-The dataset is sourced from 16 globally recognized medical entities, covering a wide range of healthcare contexts and audiences. 
-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.
+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. 
+
+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. 
+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). 
 Article length varies widely from very short statements to 100+ page guides. 
 
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+The dataset is sourced from 16 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)
+
 
 #### Data Collection and Processing
 
@@ -129,23 +114,25 @@ This filtering procedure was performed differently for each source using a sampl
 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. 
 
-#### Who are the source data producers?
+#### Personal and Sensitive Information
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
 
-We employed pragmatic selection criteria over medical sources, seeking CPGs that were: 
+As the articles are publicly accessible, no personal or sensitive information is included.
 
-- (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)
+## Uses
 
+<!-- Address questions around how the dataset is intended to be used. -->
 
-#### Personal and Sensitive Information
+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.
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+- **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.
 
-As the articles are publicly accessible, no personal or sensitive information is included.
 
 ## Bias, Risks, and Limitations
 
@@ -167,6 +154,7 @@ We encourage users of this content to be mindful of its current limitations in t
 ## 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