ArticleBMC public health2022
Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm.
Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
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- Prediction models based on machine learning algorithms for COVID-19 severity risk.BMC public health · 2025Article
- In-hospital mortality predictors among COVID-19 patients in the West Bank, Palestine: a multi-center retrospective cohort study.Frontiers in medicine · 2025Article
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- Predicting higher risk factors for COVID-19 short-term reinfection in patients with rheumatic diseases: a modeling study based on XGBoost algorithm.Journal of translational medicine · 2024Article
- Identification of Risk Factors for Relapse in Childhood Leukemia Using Penalized Semi-parametric Mixture Cure Competing Risks Model.Journal of research in health sciences · 2024Article
- Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study.BMC medical research methodology · 2023Article
- Comparison the effects and side effects of Covid-19 vaccination in patients with inflammatory bowel disease (IBD): a systematic scoping review.BMC gastroenterology · 2022Article
- A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome.Frontiers in nutrition · 2022Article
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Abstract
backgroundNarrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms.
methodsThe retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability.
resultsThe empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO
conclusionThe results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO
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