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The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining.
Zhan, Ge; Yang, Fuming; Zhang, Liangbo; Wang, Hanfeng.
  • Zhan G; AI Data Analytics Lab, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU United International College), Zhuhai 519087, China.
  • Yang F; Division of Science & Technology, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU United International College), Zhuhai 519087, China.
  • Zhang L; School of Economics and Management, Harbin Institute of Technology Shenzhen, Shenzhen 518000, China.
  • Wang H; Division of Science & Technology, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU United International College), Zhuhai 519087, China.
Healthcare (Basel) ; 10(10)2022 Oct 18.
Article in English | MEDLINE | ID: covidwho-2082335
ABSTRACT
We developed a workflow for the search and screening of natural products by drawing from worldwide experiences shared by online platform users, illustrated how to cope with COVID-19 with a text-mining approach, and statistically tested the natural product identified. We built a knowledge base, which consists of three ontologies pertaining to 7653 narratives. Mustard emerged from texting mining and knowledge engineering as an important candidate relating to COVID-19 outcomes. The findings indicate that, after controlling for the containment index, the net import of mustard is related with reduced total and new deaths of COVID-19 for the non-vaccination time period, with considerable effect size (>0.2).
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Qualitative research / Randomized controlled trials Topics: Vaccines Language: English Year: 2022 Document Type: Article Affiliation country: Healthcare10102071

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Qualitative research / Randomized controlled trials Topics: Vaccines Language: English Year: 2022 Document Type: Article Affiliation country: Healthcare10102071