Demystifying COVID-19 mortality causes with interpretable data mining.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 May 2024
02 May 2024
Historique:
received:
31
01
2024
accepted:
28
04
2024
medline:
3
5
2024
pubmed:
3
5
2024
entrez:
2
5
2024
Statut:
epublish
Résumé
While COVID-19 becomes periodical, old individuals 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, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 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. Patients with "C-reactive protein > 8 mg/L", "neutrophils percentage > 75.0 %", "lymphocytes percentage < 20%", and "albumin < 40 g/L" have a 2
Identifiants
pubmed: 38698064
doi: 10.1038/s41598-024-60841-w
pii: 10.1038/s41598-024-60841-w
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10076Subventions
Organisme : National Key Research and Development Program of China
ID : 2022YFC2009800
Informations de copyright
© 2024. The Author(s).
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