Demystifying COVID-19 Mortality Causes with Interpretable Data Mining

Demystifying COVID-19 Mortality Causes with Interpretable Data Mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Demystifying COVID-19 Mortality Causes with Interpretable Data Mining Xinyu Qian, Zhihong Zuo, Danni Xu, Shanyun He, Conghao Zhou, Zhanwen Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3912968/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract While SARS-CoV-2 infection rates are declining, older adults remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, such as bilirubin, organ failure, patient age, and underlying disease, they fail to provide a comprehensive analysis to reveal their relationships and interactive effects on the risk of death. Based on the demographic information, inspection indicators, and underlying diseases of 1917 patients (102 were dead) admitted to Xiangya Hospital over a 4-month period, we used the association rule mining method to identify the risk factors leading causes of death among elderly Omicron patients. Firstly, we used the Affinity Propagation clustering to extract key features such as blood parameters, liver function indicators, renal function indicators, coagulation function indicators, and underlying diseases affecting death from the dataset. Then, we applied the Apriori to obtain 7 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. For instance, patients with “C-reactive protein > 8 mg/L”, “neutrophils percentage > 75.0 %”, “lymphocytes percentage < 20 %”, and “albumin 0.5 mg/L” and “WBC > 9.5 * 10 9 /L” are continuously included in this foundation, the mortality rate can be increased to 3x or 4x. In addition, we also found that liver and kidney diseases significantly affect patient mortality. Given patients with liver and renal diseases associated with other abnormal features, their mortality rate can be as high as 100 %. These findings can support auxiliary diagnosis and treatment to, facilitate early intervention in patients, thereby reducing patient mortality. Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryinformationV2.pdf Cite Share Download PDF Status: Published Journal Publication published 02 May, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Mar, 2024 Reviews received at journal 05 Mar, 2024 Reviewers agreed at journal 26 Feb, 2024 Reviewers agreed at journal 19 Feb, 2024 Reviewers invited by journal 19 Feb, 2024 Editor assigned by journal 19 Feb, 2024 Editor invited by journal 16 Feb, 2024 Submission checks completed at journal 16 Feb, 2024 First submitted to journal 31 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3912968","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":273471463,"identity":"2935ed3e-bbe5-445c-9dc9-5b4b2cc2283d","order_by":0,"name":"Xinyu Qian","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Qian","suffix":""},{"id":273471464,"identity":"e1ae9bd1-eb85-4f52-806e-456edeed1067","order_by":1,"name":"Zhihong Zuo","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhihong","middleName":"","lastName":"Zuo","suffix":""},{"id":273471465,"identity":"5f79a4fd-e03a-4a67-9192-483554700056","order_by":2,"name":"Danni Xu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Danni","middleName":"","lastName":"Xu","suffix":""},{"id":273471466,"identity":"978ae97b-3471-4048-a6f6-bbf5e576536b","order_by":3,"name":"Shanyun He","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shanyun","middleName":"","lastName":"He","suffix":""},{"id":273471467,"identity":"5d0e9d07-bc70-4b7d-bb1a-151b1d8a4fb4","order_by":4,"name":"Conghao Zhou","email":"","orcid":"","institution":"University of Waterloo","correspondingAuthor":false,"prefix":"","firstName":"Conghao","middleName":"","lastName":"Zhou","suffix":""},{"id":273471468,"identity":"ab513a75-ced2-45b6-b404-611094dc50e5","order_by":5,"name":"Zhanwen Wang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhanwen","middleName":"","lastName":"Wang","suffix":""},{"id":273471469,"identity":"ee3f0141-238d-485e-b463-38dde096e66f","order_by":6,"name":"Shucai Xie","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shucai","middleName":"","lastName":"Xie","suffix":""},{"id":273471470,"identity":"9d0e0a0f-5be5-4516-9045-b9cdb7da115f","order_by":7,"name":"Yongmin Zhang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yongmin","middleName":"","lastName":"Zhang","suffix":""},{"id":273471471,"identity":"4b7a4f4e-121b-414d-9aef-347d188f7137","order_by":8,"name":"Fan Wu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wu","suffix":""},{"id":273471472,"identity":"0e1f9ca3-47b4-4ee2-a8b3-001a5e8db4d3","order_by":9,"name":"Feng Lyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBAC9gYg8YCBQQ7CZSNCC88BIJHAwGBMupbEBuK1sPcefpHYdji9v/+MAcOHssMM/LMbCGjhOZdmAdSSO+NGjgHjjHOHGSTuHMCvxV4ix8wApGWDBI8BM2/bYQYDiQQCtsi/AWtJN+A/Y8D8lygtEjzGD4BaEgwYcgyYGYnSwpNjxpBwLt1wxo20goM959J5JG4Q0sJ+xvjDhzJref7+wxsf/CizluOfQUALELBJMEKj4wDIDILqgYD5A8MfYtSNglEwCkbBiAUAwYxBZc32+pcAAAAASUVORK5CYII=","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Lyu","suffix":""},{"id":273471473,"identity":"57d37407-9001-4961-ae25-6f4b5b5fd5bf","order_by":10,"name":"Lina Zhang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Zhang","suffix":""},{"id":273471474,"identity":"0d74d2ea-ad44-439c-b1d3-65d43dc15bed","order_by":11,"name":"Zhaoxin Qian","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoxin","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2024-01-31 07:30:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3912968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3912968/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-60841-w","type":"published","date":"2024-05-02T19:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56043062,"identity":"8413a763-6321-4fac-ae5b-d9cae2ec9f4d","added_by":"auto","created_at":"2024-05-07 20:10:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":347547,"visible":true,"origin":"","legend":"","description":"","filename":"Article0216.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3912968/v1_covered_4da4e83f-ea84-4743-89c0-2678f478b667.pdf"},{"id":51324684,"identity":"c09f27fd-a77f-4cec-bc9b-98ff3df954d1","added_by":"auto","created_at":"2024-02-19 15:44:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":108001,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3912968/v1/bb81336e91e6f51f163f7131.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demystifying COVID-19 Mortality Causes with Interpretable Data Mining","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3912968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3912968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"While SARS-CoV-2 infection rates are declining, older adults remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, such as bilirubin, organ failure, patient age, and underlying disease, they fail to provide a comprehensive analysis to reveal their relationships and interactive effects on the risk of death. Based on the demographic information, inspection indicators, and underlying diseases of 1917 patients (102 were dead) admitted to Xiangya Hospital over a 4-month period, we used the association rule mining method to identify the risk factors leading causes of death among elderly Omicron patients. Firstly, we used the Affinity Propagation clustering to extract key features such as blood parameters, liver function indicators, renal function indicators, coagulation function indicators, and underlying diseases affecting death from the dataset. Then, we applied the Apriori to obtain 7 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. For instance, patients with “C-reactive protein \u003e 8 mg/L”, “neutrophils percentage \u003e 75.0 %”, “lymphocytes percentage \u003c 20 %”, and “albumin \u003c 40 g/L” have a 2x mortality rate than the basic one. If the characteristics of “D-dimer \u003e 0.5 mg/L” and “WBC \u003e 9.5 * 10 9 /L” are continuously included in this foundation, the mortality rate can be increased to 3x or 4x. In addition, we also found that liver and kidney diseases significantly affect patient mortality. Given patients with liver and renal diseases associated with other abnormal features, their mortality rate can be as high as 100 %. These findings can support auxiliary diagnosis and treatment to, facilitate early intervention in patients, thereby reducing patient mortality.","manuscriptTitle":"Demystifying COVID-19 Mortality Causes with Interpretable Data Mining","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 15:44:00","doi":"10.21203/rs.3.rs-3912968/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-20T10:32:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-05T14:15:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0483a59b-b47a-4583-9a7f-8057d972a550","date":"2024-02-26T16:08:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34715e73-7046-4da4-9705-1a2f0a3c3168","date":"2024-02-19T13:58:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-19T13:16:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-19T13:10:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-16T12:37:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-16T12:34:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-31T07:21:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44a53fdf-78e9-4c1f-a73e-5ed3d0c219a5","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-07T20:08:31+00:00","versionOfRecord":{"articleIdentity":"rs-3912968","link":"https://doi.org/10.1038/s41598-024-60841-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-05-02 19:57:53","publishedOnDateReadable":"May 2nd, 2024"},"versionCreatedAt":"2024-02-19 15:44:00","video":"","vorDoi":"10.1038/s41598-024-60841-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-60841-w","workflowStages":[]},"version":"v1","identity":"rs-3912968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3912968","identity":"rs-3912968","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}