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CC BY-NC-ND 4.0 · Yearb Med Inform 2021; 30(01): 245-256
DOI: 10.1055/s-0041-1726483
Section 10: Natural Language Processing
Working Group Contribution

Investigating the Scientific ‘Infodemic’ Phenomenon Related to the COVID-19 Pandemic

- a Position Paper from the IMIA Working Group on ˝Language and Meaning in BioMedicine”
László Balkányi
1   Medical Informatics Research and Development Center (MIRDC), Pannon University, Veszprém, Hungary
,
Lajos Lukács
2   DSS Consulting, Ltd. Budapest, Hungary
,
Ronald Cornet
3   Department of Medical Informatics, Amsterdam University Medical Center - University of Amsterdam, Amsterdam Public Health research institute, Amsterdam, The Netherlands
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Summary

Objectives: The 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.

Methods: A 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.

Results: Never 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.

Keywords

COVID-19 pandemic - review literature - metrics - FAIR principles - infodemic

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Publication History

Article published online:
21 April 2021

© 2021. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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