Interpretable machine learning models for predicting short-term prognosis in AChR-Ab+ generalized myasthenia gravis using clinical features and systemic inflammation index
Our analysis confirms that the XGBoost model, integrating clinical indicators with systemic inflammation indexes, effectively predicts short-term prognosis in AChR-Ab+ GMG patients. This approach enhances clinical decision-making and improves patient outcomes.
Authors
- Qi Li (ORCID: https://orcid.org/0000-0002-5528-1626)
- Yanan Xu (ORCID: https://orcid.org/0009-0003-9361-0819)
- Wenbin Wang (ORCID: https://orcid.org/0000-0002-0582-8742)
- Xiao‐Bin Jia (ORCID: https://orcid.org/0000-0003-0471-8258)
- Meng Pan (ORCID: https://orcid.org/0000-0003-4947-9369)
- Liqin Luan
- Qiqi Guo
Institutions
- Xuzhou Medical College (CN)
- Nanjing Brain Hospital (CN)
- Nanjing Jiangning Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-10-09
- DOI
- https://doi.org/10.3389/fneur.2024.1459555
- Citations
- 3
- Primary Topic
- Myasthenia Gravis and Thymoma
- Type
- article
- Field-Weighted Citation Impact
- 1.11