AI Article Synopsis
- A Kaggle challenge during COVID-19 prompted the creation of a repository focused on nursing literature related to COVID-19 and historical epidemics, aiming to establish a similar resource as the CORD-19 dataset.
- The Nursing COVID and Historical Epidemic Literature repository, utilizing data from CORD-19 and LitCOVID, features abstracts of nurse-specific articles summarized through text mining algorithms.
- As of July 2020, the repository includes 760 articles highlighting key themes such as the need for psychological support for nurses and the value of rapid education in response to health crises.
Article Abstract
Background: During COVID-19, a Kaggle challenge was issued to data scientists to leverage text mining to provide high-level summaries of full-text articles in the COVID-19 Open Research Dataset (CORD-19) data set, a data set containing articles around COVID-19 and other epidemics. A question was asked: "What if nursing had something similar?"
Purpose: Describe the development and function of the Nursing COVID and Historical Epidemic Literature and describe high-level summaries of abstracts within the repository.
Method: Nurse-specific literature was abstracted from two data sets: CORD-19 and LitCOVID. LitCOVID is a data set containing the most up-to-date literature around COVID-19. Multiple text mining algorithms were utilized to provide summaries of the articles.
Discussion: As of July 2020, the repository contains 760 articles. Summaries indicate the importance of psychological support for nurses and of high-impact rapid education.
Conclusion: To our knowledge, this repository is the only repository specific for nursing that utilizes text mining to provide summaries.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846882 | PMC |
| http://dx.doi.org/10.1016/j.outlook.2020.12.017 | DOI Listing |
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