Explainable Machine Learning Models for Prediction of Surgical Site Infection After Posterior Lumbar Fusion Surgery Based on Shapley Additive Explanations
ML-based prediction tools can accurately assess the risk of SSI after posterior lumbar fusion surgery. Additionally, ML combined with SHAP could provide a clear interpretation of individualized risk prediction and give physicians an intuitive comprehension of the effects of the model's essential features.
Authors
- J. C. Wang
- Zhi‐Yang Xie (ORCID: https://orcid.org/0000-0001-8716-7134)
- Yili Hu (ORCID: https://orcid.org/0000-0003-1074-8779)
- PeiYang Wang (ORCID: https://orcid.org/0000-0002-4298-8147)
- GuanRui Ren (ORCID: https://orcid.org/0000-0003-2438-8081)
- MeiJi Shen
- Lei Liu (ORCID: https://orcid.org/0000-0002-9714-2130)
- Hui Wang (ORCID: https://orcid.org/0000-0002-5715-0638)
- Yuntao Wang (ORCID: https://orcid.org/0000-0003-1805-1247)
- Xiao-Tao Wu
Institutions
- Zhongda Hospital Southeast University (CN)
Publication Details
- Journal
- World Neurosurgery
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1016/j.wneu.2025.123942
- Citations
- 1
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.67