TY - JOUR
T1 - Investigating the Scientific 'Infodemic' Phenomenon Related to the COVID-19 Pandemic
AU - Balkányi, L. szló
AU - Lukács, Lajos
AU - Cornet, Ronald
N1 - Publisher Copyright: 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.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85115898704&partnerID=8YFLogxK
U2 - https://doi.org/10.1055/s-0041-1726483
DO - https://doi.org/10.1055/s-0041-1726483
M3 - Article
C2 - 33882597
SN - 0943-4747
VL - 30
SP - 245
EP - 256
JO - Yearbook of medical informatics
JF - Yearbook of medical informatics
IS - 1
ER -