Cargando…
Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran
BACKGROUND AND AIMS: Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims...
Autores principales: | Yazdani, Azita, Bigdeli, Somayeh Kianian, Zahmatkeshan, Maryam |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099201/ https://www.ncbi.nlm.nih.gov/pubmed/37064314 http://dx.doi.org/10.1002/hsr2.1212 |
Ejemplares similares
-
Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data
por: Yazdani, Azita, et al.
Publicado: (2022) -
Predictive modeling for COVID-19 readmission risk using machine learning algorithms
por: Shanbehzadeh, Mostafa, et al.
Publicado: (2022) -
The use of mobile health interventions for gestational diabetes mellitus: a descriptive literature review
por: Zahmatkeshan, Maryam, et al.
Publicado: (2021) -
Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms
por: Yazdani, Azita, et al.
Publicado: (2023) -
ART Registries–Characteristics and experiences: A comparative study
por: Zahmatkeshan, Maryam, et al.
Publicado: (2019)