Predicting higher risk factors for COVID-19 short-term reinfection in patients with rheumatic diseases: a modeling study based on XGBoost algorithm
Abstract Background Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short...
Saved in:
| Main Authors: | Yao Liang, Siwei Xie, Xuqi Zheng, Xinyu Wu, Sijin Du, Yutong Jiang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
|
| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-024-05982-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
by: Shuang Zeng, et al.
Published: (2025-01-01) -
XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study
by: Zijuan Fan, et al.
Published: (2024-12-01) -
Primer on the Rheumatic Diseases /
Published: (1993) -
XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites
by: Salman Khan, et al.
Published: (2025-02-01) -
Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States
by: Yuanchao Li, et al.
Published: (2024-12-01)