Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis.

Abstract

Authors

• Michael W Fields
  Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
• Jay Zaifman
  Department of Orthopaedic Surgery, New York University Langone Health, New York, NY, USA.
• Matan S Malka
  Department of Orthopaedic Surgery, Columbia University, New York, NY, USA. msm2244@cumc.columbia.edu.
• Nathan J Lee
  Department of Orthopaedics, Columbia University Medical Center, The Och Spine Hospital at New York-Presbyterian, New York, NY, USA. Electronic address: njl2116@cumc.columbia.edu.
• Christina C Rymond
  Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
• Matthew E Simhon
  Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
• Theodore Quan
  George Washington University School of Medicine, Washington, DC 20037, USA.
• Benjamin D Roye
  Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
• Michael G Vitale
  Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.

Keywords

External Resources