Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique

Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique

Abstract Background Machine learning (ML) has been widely applied to predict the outcomes of numerous diseases. The current study aimed to develop a prognostic prediction model using machine learning algorithms and identify risk factors associated with residual back pain in patients with osteoporoti...

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Bibliographic Details
Main Authors: Hao Wu, Chao Li, Jiajun Song, Jiaming Zhou
Format: Article
Language:English
Published: BMC 2024-11-01
Series:Journal of Orthopaedic Surgery and Research
Subjects:
Visual analog scale
Machine learning
Percutaneous vertebroplasty
Residual back pain
Prognostic prediction
Osteoporotic vertebrae compression fracture
Online Access:https://doi.org/10.1186/s13018-024-05271-0
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https://doi.org/10.1186/s13018-024-05271-0

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