Interpretable Machine Learning Model for Predicting the Prognosis of Guillain-Barré Syndrome Patients
The combination of NLR, age, mechanical ventilation, hyporeflexia and abnormal glossopharyngeal vagus used to predict short-term prognosis in patients with GBS has a good predictive value.
Authors
- Ruirui Dong (ORCID: https://orcid.org/0000-0002-0464-4781)
- Fan Yang (ORCID: https://orcid.org/0000-0002-7883-9573)
- Junshuang Guo
- Ruike Zhang (ORCID: https://orcid.org/0000-0002-9391-5060)
- Yating Wang (ORCID: https://orcid.org/0000-0002-2030-6176)
- Wang Miao (ORCID: https://orcid.org/0000-0003-2289-2962)
Institutions
- Central South University (CN)
- Changsha Medical University (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Journal of Inflammation Research
- Published
- 2024-09-01
- DOI
- https://doi.org/10.2147/jir.s471626
- Citations
- 4
- Primary Topic
- Peripheral Neuropathies and Disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.48
Funders
- Zhengzhou University