Jul 2026· European spine journal· 0 citations· 12 references
Medicine
TL;DR
RF-based models can accurately and equitably predict perioperative complications in diverse spine surgery contexts, supporting personalized counseling, targeted monitoring, and optimized resource allocation.
An interpretable gradient-boosting model may support risk-stratified perioperative assessment for elderly patients undergoing abdominal surgery and Prospective multicenter validation is required before routine clinical implementation.
Qiang Zhong, Guiming Huang, Wen Zhou et al.· Frontiers in Surgery· 0 citations
The machine learning model developed in this study demonstrates strong capability in stratifying anesthetic risk for patients with lumbar spinal stenosis, providing valuable reference for selecting surgical and anesthetic approaches.
Ji-Tao Yang, Yi-Xi Wang, Qi-Hao Chen et al.· Frontiers in Medicine· 0 citations
This interpretable 11-variable model enables early POP risk stratification after brain tumor surgery and may support timely preventive intervention in neurosurgical care.
Hong-Liang Mao, Feng-Chun Mu, Xin-Yu Wang et al.· Frontiers in Cellular and In...· 0 citations
An interpretable machine learning model incorporating clinical and radiological features to predict the risk of adverse outcomes in patients with lumbar disc herniation (LDH) following unilateral biportal endoscopic (UBE) surgery was developed and validated.
Yue-Long Tan, Yu Xi, Nai-Yan Hu et al.· Frontiers in Surgery· 0 citations
An explainable RF model to predict in-hospital mortality after ATAAD surgery is developed and internally validated and is best regarded as a promising preliminary rule-out/triage tool that requires multicenter external validation before clinical use.
Ke-Yan Liu, Sili Shan, Hao-Long Zeng et al.· Frontiers in Medicine· 0 citations
HAC risk after emergency tibial fracture surgery in older adults was driven primarily by geriatric vulnerability rather than fracture-specific factors, and a simplified admission-time score may support targeted prevention, pending external validation.
Akio Shimizu, Ryota Sakamoto, H. Nagayama et al.· Aging Clinical and Experimen...· 0 citations
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