ArticleYearbook of medical informatics2021
Investigating the Scientific 'Infodemic' Phenomenon Related to the COVID-19 Pandemic.
Article in Yearbook of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe study aims at understanding the structural characteristics and content features of COVID-19 literature and public health data from the perspective of the 'Language and Meaning in Biomedicine' Working Group (LaMB WG) of IMIA. The LaMB WG has interest in conceptual characteristics, transparency, comparability, and reusability of medical information, both in science and practice.
methodsA set of methods were used (i) investigating the overall speed and dynamics of COVID-19 publications; (ii) characterizing the concepts of COVID-19 (text mining, visualizing a semantic map of related concepts); (iii) assessing (re)usability and combinability of data sets and paper collections (as textual data sets), and checking if information is Findable, Accessible, Interoperable, and Reusable (FAIR). A further method tested practical usability of FAIR requirements by setting up a common data space of epidemiological, virus genetics and governmental public health measures' stringency data of various origin, where complex data points were visualized as scatter plots.
resultsNever before were that many papers and data sources dedicated to one pandemic. Worldwide research shows a plateau at ∼ 2,200 papers per week - the dynamics of areas of studies being slightly different. Ratio of epidemic modelling is rather low (∼1%). A few 'language and meaning' methods, such as using integrated terminologies, applying data and metadata standards for processing epidemiological and case-related clinical information and in general, principles of FAIR data handling could contribute to better results, such as improved interoperability and meaningful knowledge sharing in a virtuous cycle of continuous improvements.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.