A Systematic Review on Machine Learning in Neurosurgery: The Future of Decision-Making in Patient Care

ÇELTİKÇİ E.

TURKISH NEUROSURGERY, cilt.28, sa.2, ss.167-173, 2018 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 28 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.5137/1019-5149.jtn.20059-17.1
  • Dergi Adı: TURKISH NEUROSURGERY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.167-173
  • Anahtar Kelimeler: Bayesian network, Logistic regression analysis, Machine learning, Neural network, Neurosurgery, Support vector machine, ARTIFICIAL NEURAL-NETWORK, SYMPTOMATIC CEREBRAL VASOSPASM, TRAUMATIC BRAIN-INJURY, OUTCOME PREDICTION, LOGISTIC-REGRESSION, SURGERY, SURVIVAL, HYDROCEPHALUS, CLASSIFICATION, GLIOBLASTOMA
  • Gazi Üniversitesi Adresli: Hayır

Özet

Current practice of neurosurgery depends on clinical practice guidelines and evidence-based research publications that derive results using statistical methods. However, statistical analysis methods have some limitations such as the inability to analyze non-linear variables, requiring setting a level of significance, being impractical for analyzing large amounts of data and the possibility of human bias. Machine learning is an emerging method for analyzing massive amounts of complex data which relies on algorithms that allow computers to learn and make accurate predictions. During the past decade, machine learning has been increasingly implemented in medical research as well as neurosurgical publications. This systematical review aimed to assemble the current neurosurgical literature that machine learning has been utilized, and to inform neurosurgeons on this novel method of data analysis.