401 How do Classification Versus Regression Machine Learning Algorithms Predict 5-Year Minimum Clinically Important Differences in QOD Lumbar Spondylolisthesis Patients?

Eunice Yang; Elan Schonfeld; Praveen V. Mummaneni; Dean Chou; Mohamad Bydon; Erica Fay Bisson; Christopher I. Shaffrey; Steven D. Glassman; Kevin T. Foley; Eric A. Potts; Mark Edwin Shaffrey; Domagoj Coric; John J. Knightly; Paul Park; Michael Y. Wang; Kai-Ming G. Fu; Jonathan Slotkin; Anthony L. Asher; Michael S. Virk; Regis W. Haid; Andrew Kai-Hong Chan

doi:10.1227/neu.0000000000003360_401

Back

401 How do Classification Versus Regression Machine Learning Algorithms Predict 5-Year Minimum Clinically Important Differences in QOD Lumbar Spondylolisthesis Patients?

Journal article

Peer reviewed

401 How do Classification Versus Regression Machine Learning Algorithms Predict 5-Year Minimum Clinically Important Differences in QOD Lumbar Spondylolisthesis Patients?

Eunice Yang, Elan Schonfeld, Praveen V. Mummaneni, Dean Chou, Mohamad Bydon, Erica Fay Bisson, Christopher I. Shaffrey, Steven D. Glassman, Kevin T. Foley, Eric A. Potts, …

Neurosurgery, Vol.71(Supplement_1), pp.95-95

2025-04

DOI: https://doi.org/10.1227/neu.0000000000003360_401

Appears in Miller School of Medicine - Latest Publications

Metrics

2 Record Views

Details

Title: 401 How do Classification Versus Regression Machine Learning Algorithms Predict 5-Year Minimum Clinically Important Differences in QOD Lumbar Spondylolisthesis Patients?
Creators: Eunice Yang
Elan Schonfeld
Praveen V. Mummaneni
Dean Chou
Mohamad Bydon
Erica Fay Bisson
Christopher I. Shaffrey
Steven D. Glassman
Kevin T. Foley
Eric A. Potts
Mark Edwin Shaffrey
Domagoj Coric
John J. Knightly
Paul Park
Michael Y. Wang
Kai-Ming G. Fu
Jonathan Slotkin
Anthony L. Asher
Michael S. Virk
Regis W. Haid
Andrew Kai-Hong Chan
Publication Details: Neurosurgery, Vol.71(Supplement_1), pp.95-95
Academic Unit: UMMG Dept of Neurological Surgery; Miller School of Medicine
Language: English
Resource Type: Journal article
Record Identifier: 991032600752102976