Clinical data and quantitative CT parameters combined with machine learning to predict short-term prognosis of severe COVID-19 in the elderly
The combination of clinical data and quantitative CT parameters with machine learning approaches is highly valuable in predicting the short-term prognosis of severe COVID-19 in the elderly. Among the various models tested, the Decision Tree algorithm-based model proved to be the most accurate and reliable in this context.
Authors
- Lifang Fan (ORCID: https://orcid.org/0000-0002-3996-497X)
- Shujian Wu
- Guoxian Chen
- Zhengyuan Xu (ORCID: https://orcid.org/0000-0001-9075-8875)
- Lei Huang (ORCID: https://orcid.org/0000-0003-4087-3677)
- Yimin Wu (ORCID: https://orcid.org/0000-0002-8249-3907)
- Xiaoyan Xu (ORCID: https://orcid.org/0000-0002-0996-6819)
Institutions
- Wannan Medical College (CN)
- First Affiliated Hospital of Wannan Medical College (CN)
- East China Normal University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e37096
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Wannan Medical College