Prognostication of Outcomes in Spontaneous Intracerebral Hemorrhage: A Propensity Score-Matched Analysis with Support Vector Machine.

Journal: World neurosurgery
PMID: 38008171

Abstract

OBJECTIVE: The role of surgery in spontaneous intracerebral hemorrhage (SICH) remains controversial. We aimed to use explainable machine learning (ML) combined with propensity-score matching to investigate the effects of surgery and identify subgroups of patients with SICH who may benefit from surgery in an interpretable fashion.

Authors

  • Mervyn Jun Rui Lim
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore. Electronic address: mervynlim@u.nus.edu.
  • Raphael Hao Chong Quek
    Department of Electrical and Computer Engineering, National University of Singapore.
  • Kai Jie Ng
    Yong Loo Lin School of Medicine, National University of Singapore.
  • Benjamin Yong-Qiang Tan
    Division of Neurology, Department of Medicine, National University Hospital, Singapore, Singapore.
  • Leonard Leong Litt Yeo
    Division of Neurology, Department of Medicine, National University Hospital, Singapore, Singapore.
  • Ying Liang Low
    Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.
  • Betsy Kar Hoon Soon
    Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.
  • Will Ne-Hooi Loh
    Department of Anesthesia, National University Hospital, Singapore, Singapore.
  • Kejia Teo
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Vincent Diong Weng Nga
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Tseng Tsai Yeo
    Division of Neurosurgery, University Surgical Centre, National University Hospital, Singapore.
  • Mehul Motani

Keywords

External Resources

Popular Topics
Recent Journals