Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis
This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:310 |
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| Enthalten in: |
Studies in health technology and informatics - 310(2024) vom: 25. Jan., Seite 1466-1467 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kang, Min-Ah [VerfasserIn] |
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| Links: |
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| Themen: |
Coronavirus |
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| Anmerkungen: |
Date Completed 26.01.2024 Date Revised 26.01.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.3233/SHTI231247 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM367601370 |
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| 520 | |a This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors | ||
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