Celtikci Emrah
University of Pittsburgh Medical Center, Department of Neurological Surgery, Pittsburgh, PA, USA.
Turk Neurosurg. 2018;28(2):167-173. doi: 10.5137/1019-5149.JTN.20059-17.1.
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 nonlinear 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.
当前神经外科的实践依赖于临床实践指南和基于证据的研究出版物，这些出版物使用统计方法得出结果。然而，统计分析方法存在一些局限性，例如无法分析非线性变量、需要设定显著性水平、分析大量数据不切实际以及存在人为偏差的可能性。机器学习是一种用于分析大量复杂数据的新兴方法，它依赖于允许计算机学习并做出准确预测的算法。在过去十年中，机器学习在医学研究以及神经外科出版物中得到了越来越多的应用。本系统综述旨在汇总目前已使用机器学习的神经外科文献，并向神经外科医生介绍这种新的数据分析方法。
