Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise

Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise

BackgroundMachine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-performing prediction models. Hidden blood loss (HBL) is prevalent during the perioperative period of spinal treatment and might result in a poor progno...

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Bibliographic Details
Main Authors: Bo Yang, Lin Gao, Xingang Wang, Jianmin Wei, Bin Xia, Xiangwei Liu, Peng Zheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Public Health
Subjects:
application
machine learning
hidden blood loss
risk factors
thoracolumbar burst fracture
Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2022.969919/full

Internet

https://www.frontiersin.org/articles/10.3389/fpubh.2022.969919/full

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