Machine learning-based prediction of intensive care unit admission in COVID-19 patients presenting with mild respiratory failure
Machine learning algorithms can reliably predict intensive care unit admission in COVID-19 patients with mild respiratory failure. These models identified key clinical and laboratory factors that may facilitate early risk stratification and guide treatment planning.
Authors
- Kamil Mert (ORCID: https://orcid.org/0000-0002-5201-7918)
- İhsan Boyacı (ORCID: https://orcid.org/0000-0001-7369-0426)
- Oktay Olmuşçelik (ORCID: https://orcid.org/0000-0002-9815-1848)
- Banu Karaalioğlu (ORCID: https://orcid.org/0000-0003-4864-5652)
- Abdullah Kansu (ORCID: https://orcid.org/0000-0001-8902-5498)
- Selda Aydın (ORCID: https://orcid.org/0000-0002-3131-442X)
- Ezgi Yılmaz (ORCID: https://orcid.org/0000-0002-4181-5674)
- Meyha Şahin (ORCID: https://orcid.org/0000-0003-4147-3587)
- Mustafa Düger (ORCID: https://orcid.org/0000-0002-4091-6465)
- Şule Ceylan (ORCID: https://orcid.org/0000-0003-1485-3701)
- Melih Akan
- Rıdvan Dumlu
- Esra Demir
- Ufuk Süleyman
- Mebrule Muğlu
- Ali Mert
- Bahadır Ceylan
Institutions
- Tokat Gaziosmanpaşa Üniversitesi (TR)
- Istanbul Medipol University (TR)
- Gaziosmanpaşa Taksim Eğitim ve Araştırma Hastanesi (TR)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-02-16
- DOI
- https://doi.org/10.3389/fmed.2026.1724947
- Primary Topic
- COVID-19 Clinical Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00