Predicting higher risk factors for COVID-19 short-term reinfection in patients with rheumatic diseases: a modeling study based on XGBoost algorithm
We developed explainable machine learning models to predict the risk of short-term COVID-19 reinfection in patients with rheumatic diseases. SHAP analysis highlighted the importance of clinical and psychological factors. Factors included anxiety, fatigue, depression, poor sleep quality, high disease activity during initial infection, and the use of glucocorticoid taper were significant predictors. These findings underscore the need for targeted preventive measures in this patient population.
Authors
- Yao Liang (ORCID: https://orcid.org/0000-0001-9309-0890)
- Xinyu Wu (ORCID: https://orcid.org/0000-0002-1117-5098)
- Shixuan Du (ORCID: https://orcid.org/0000-0001-9323-1307)
- Yutong Jiang (ORCID: https://orcid.org/0000-0001-6558-4201)
- Xuqi Zheng (ORCID: https://orcid.org/0000-0002-1818-3605)
- Siwei Xie (ORCID: https://orcid.org/0009-0000-1602-2296)
Institutions
- Johns Hopkins University (US)
- Sun Yat-sen University (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1186/s12967-024-05982-2
- Citations
- 1
- Primary Topic
- COVID-19 Clinical Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.39
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University