Multiple Sclerosis Pubmed Abstracts with SDoH relation Nov 2024

Citation Author(s):
Tim
Breitenfelder
University of Oxford
Frederic
Andres
National institute of informatics
Amgad
Mahmoud
The British University in Egypt
Andreas
Pester
The British University in Egypt
Shihori
Tanabe
National Institute of Science Health
Hesham H.
Ali
University of Nebraska at Omaha
Submitted by:
Andres Frederic
Last updated:
Mon, 11/11/2024 - 01:28
DOI:
10.21227/bgd6-ar29
Data Format:
*.json;
License:
Creative Commons Attribution
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Categories:
Biomedical and Health Sciences
Keywords:
multiple sclerosis, NLP, Biomedical and Health Sciences
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Abstract 

This dataset comprises a comprehensive collection of PubMed abstracts and associated metadata focusing on the topic of multiple sclerosis (MS) in relation to social determinants and environmental factors, spanning publications from January 1, 2018, to December 31, 2023. The data was meticulously gathered using the PubMed E-Utilities API with the search query "multiple sclerosis" AND ("social determinants" OR "environmental factors"). Articles classified as preprints were excluded to ensure the inclusion of peer-reviewed research only.

This curated dataset serves as a valuable resource for researchers, clinicians, and policymakers interested in exploring the interplay between multiple sclerosis and socio-environmental factors. It facilitates literature reviews, trend analyses, and supports the development of interventions aimed at addressing the social and environmental determinants of health in the context of MS.

Keywords: Multiple Sclerosis, Social Determinants, Environmental Factors, PubMed Abstracts, Dataset, Biomedical Research, Literature Review

Instructions: 

Each entry in the dataset includes:

  • ID: A unique internal identifier for each article.
  • Title: The title of the research article.
  • Authors: A list of authors associated with the article.
  • Year: The publication year.
  • Abstract: The full abstract text.

The dataset was assembled by first retrieving all relevant PubMed IDs (PMIDs) matching the search criteria. Subsequent detailed information for each PMID was fetched in batches to optimize the data retrieval process. The extraction focused on key elements such as publication date, article type, title, authorship, and abstract content.

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