ArticleLife (Basel, Switzerland)2022
Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE).
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- A flexible bounded stochastic framework for uncertainty and reliability in physical systems.Scientific reports · 2026Article
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.Scientific reports · 2025Article
- Virology Applications to the COVID-19 Pandemic.Life (Basel, Switzerland) · 2025Article
- Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Healthcare systems have been under immense pressure since the beginning of the COVID-19 pandemic; hence, studies on using machine learning (ML) methods for classifying ICU admissions and resource allocation are urgently needed. We investigated whether ML can propose a useful classification model for predicting the ICU admissions of COVID-19 patients. In this retrospective study, the clinical characteristics and laboratory findings of 100 patients with laboratory-confirmed COVID-19 tests were retrieved between May 2020 and January 2021. Based on patients' demographic and clinical data, we analyzed the capability of the proposed weighted radial kernel support vector machine (SVM), coupled with
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Registered trials
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