Predicting the Outcome and Survival of Patients with Spinal Cord Injury Using Machine Learning Algorithms: A Systematic Review.

Journal: World neurosurgery
PMID: 38796146

Abstract

BACKGROUND: Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurologic outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI.

Authors

  • Mohammad Amin Habibi
    Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Seyed Ahmad Naseri Alavi
    Department of Neurosurgery, School of Medicine, Emory University, Atlanta, GA, USA. Electronic address: dr.arsalan2010@gmail.com.
  • Ali Soltani Farsani
    School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Mohammad Mehdi Mousavi Nasab
    Faculty of Medicine, Candidate Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Zohreh Tajabadi
    Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
  • Andrew J Kobets
    Department of Neurological Surgery, Montefiore Medical, Bronx, NY, USA.

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