Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach.

Journal: World neurosurgery
PMID: 33212282

Abstract

OBJECTIVE: Mortality following surgical resection of spinal tumors is a devastating outcome. Naïve Bayes machine learning algorithms may be leveraged in surgical planning to predict mortality. In this investigation, we use a Naïve Bayes classification algorithm to predict mortality following spinal tumor excision within 30 days of surgery.

Authors

  • Kevin J DiSilvestro
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Ashwin Veeramani
    Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA.
  • Christopher L McDonald
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Andrew S Zhang
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Eren O Kuris
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Wesley M Durand
    1Division of Spine Surgery and.
  • Eric M Cohen
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Alan H Daniels
    1Division of Spine Surgery and.

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