Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Web-based machine learning application for interpretable prediction of prolonged length of stay after lumbar spinal stenosis surgery: a retrospective cohort study with explainable AI
4
Zitationen
6
Autoren
2025
Jahr
Abstract
Machine learning in association with SHAP and LIME can provide a clear explanation of personalized risk prediction, and spine surgeons can gain a perceptual grasp of the impact of important model components. Utilization and future clinical research of our RF model are made simple and accessible through the web application.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.514 Zit.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
2020 · 7.637 Zit.
Calculation of average PSNR differences between RD-curves
2001 · 4.088 Zit.
Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration
2001 · 3.882 Zit.
Vertebral fracture assessment using a semiquantitative technique
1993 · 3.601 Zit.