ArticleFrontiers in medicine2021
Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Automatic phenotyping of emergency department patients with incidental hepatic steatosis: A machine learning clustering analysis.The American journal of emergency medicine · 2026Article
- Epidemiological Analysis of the COVID-19 Clusters in the Early Stages of the Epidemic in Shanghai, China: Pandemic-to-Epidemic Response Shift.Tropical medicine and infectious disease · 2025Article
- Clinical characteristics of COVID-19 in children and adolescents: insights from an Italian paediatric cohort using a machine-learning approach.BMJ public health · 2025Article
- Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19.Frontiers in public health · 2025Article
- Data-driven health deficit assessment improves a frailty index's prediction of current cognitive status and future conversion to dementia: results from ADNI.GeroScience · 2023Article
- Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era.Life (Basel, Switzerland) · 2022Article
- Clinical-epidemiologic variation in patients treated in the first and second wave of COVID-19 in Lambayeque, Peru: A cluster analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022Article
- Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study.PloS one · 2022Observational
- Anti-Ma2 Antibody-Associated Paraneoplastic Neurological Syndromes: A Pilot Study.Brain sciences · 2021Article
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12 authors.
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Abstract
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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