期刊
FRONTIERS IN MEDICINE
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2021.644724
关键词
COVID-19; cluster analysis; factor analysis of mixed data; symptoms; laboratory findings; support vector machine
资金
- National Natural Science Foundation of China [81874383]
The study identified three clusters of COVID-19 patients with distinct clinical characteristics using FAMD-based cluster analysis. The results showed consistency between symptoms and laboratory findings of COVID-19 patients, and revealed differences in hospital stay durations and survival among the three clusters based on severity of clinical characteristics. A classifier model was constructed to provide better individualized treatments for future COVID-19 patients.
The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.
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