Machine learning approaches to predict the need for intensive care unit admission among Iranian COVID‐19 patients based on ICD‐10: A cross‐sectional study
Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID-19 patients. Machine learning models could help quickly identify high-risk patients upon entry and reduce mortality and morbidity among COVID-19 patients.
Authors
- Amirhossein Aghakhani (ORCID: https://orcid.org/0000-0003-1804-6034)
- Jaleh Shoshtarian Malak (ORCID: https://orcid.org/0000-0001-6373-0578)
- Hojjat Zeraati (ORCID: https://orcid.org/0000-0002-8635-4322)
- Mohammad Sadeq Najafi (ORCID: https://orcid.org/0000-0002-5041-6798)
- Mir Saeed Yekaninejad (ORCID: https://orcid.org/0000-0003-3648-5276)
- Hamid Ariannejad (ORCID: https://orcid.org/0000-0002-6958-007X)
- Zahra Karimi (ORCID: https://orcid.org/0000-0001-7840-0432)
Institutions
- Iran University of Medical Sciences (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- Health Science Reports
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1002/hsr2.70041
- Citations
- 1
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.40