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.
Michael W FieldsJay ZaifmanMatan S MalkaNathan J LeeChristina C RymondMatthew E SimhonTheodore QuanBenjamin D RoyeMichael G VitalePublished in: Spine deformity (2024)
Machine learning algorithms accurately predict extended LOS across a national patient cohort and characterize key preoperative drivers of increased LOS after PSIF in pediatric patients with EOS.
Keyphrases
- machine learning
- early onset
- end stage renal disease
- artificial intelligence
- late onset
- newly diagnosed
- ejection fraction
- minimally invasive
- chronic kidney disease
- big data
- deep learning
- prognostic factors
- patients undergoing
- case report
- peritoneal dialysis
- coronary artery bypass
- coronary artery disease
- young adults
- acute coronary syndrome
- percutaneous coronary intervention