Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach.
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| Title: | Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. |
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| Authors: | Wong KC; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China., Xiang Y; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China., Yin L; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China., So HC; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China.; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Kunming, China.; CUHK Shenzhen Research Institute, Shenzhen, China.; Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.; Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong, China.; Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China.; Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong, China. |
| Source: | JMIR public health and surveillance [JMIR Public Health Surveill] 2021 Sep 30; Vol. 7 (9), pp. e29544. Date of Electronic Publication: 2021 Sep 30. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101669345 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-2960 (Electronic) Linking ISSN: 23692960 NLM ISO Abbreviation: JMIR Public Health Surveill Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 34591027 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Wong+KC%22">Wong KC</searchLink>; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China.<br /><searchLink fieldCode="AU" term="%22Xiang+Y%22">Xiang Y</searchLink>; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China.<br /><searchLink fieldCode="AU" term="%22Yin+L%22">Yin L</searchLink>; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China.<br /><searchLink fieldCode="AU" term="%22So+HC%22">So HC</searchLink>; School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China.; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Kunming, China.; CUHK Shenzhen Research Institute, Shenzhen, China.; Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.; Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong, China.; Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China.; Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong, China. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101669345%22">JMIR public health and surveillance</searchLink> [JMIR Public Health Surveill] 2021 Sep 30; Vol. 7 (9), pp. e29544. <i>Date of Electronic Publication: </i>2021 Sep 30. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22JMIR+Publications%22">JMIR Publications </searchLink><i>Country of Publication: </i>Canada <i>NLM ID: </i>101669345 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2369-2960 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2223692960%22">23692960 </searchLink><i>NLM ISO Abbreviation: </i>JMIR Public Health Surveill <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=34591027 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2196/29544 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: e29544 Titles: – TitleFull: Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wong KC – PersonEntity: Name: NameFull: Xiang Y – PersonEntity: Name: NameFull: Yin L – PersonEntity: Name: NameFull: So HC IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 09 Text: 2021 Sep 30 Type: published Y: 2021 Identifiers: – Type: issn-electronic Value: 2369-2960 Numbering: – Type: volume Value: 7 – Type: issue Value: 9 Titles: – TitleFull: JMIR public health and surveillance Type: main |
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