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

Journal: Journal of orthopaedic surgery and research

PMID: 39609923

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 osteoporotic vertebrae compression fracture (OVCF) following percutaneous vertebroplasty (PVP).

Authors

Hao Wu

Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
Chao Li

McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
Jiajun Song

Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Jiaming Zhou

Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China.

Keywords

Aged Aged, 80 and over Back Pain Female Fractures, Compression Humans Lumbar Vertebrae Machine Learning Male Middle Aged Osteoporotic Fractures Predictive Value of Tests Prognosis Retrospective Studies Risk Factors Spinal Fractures Thoracic Vertebrae Vertebroplasty

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View on PubMed Access via DOI PubMed (39609923)

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

Abstract

Authors

Keywords

External Resources

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