Enhancing clinical decision-making in closed pelvic fractures with machine learning models

Enhancing clinical decision-making in closed pelvic fractures with machine learning models

Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML) algorithms to predict HI and death in patients wi...

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Bibliographic Details
Main Authors: Dian Wang, Yongxin Li, Li Wang
Format: Article
Language:English
Published: Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina 2024-11-01
Series:Biomolecules & Biomedicine
Subjects:
Hemodynamic instability
HI
closed pelvic fracture
PF
machine learning
ML
Online Access:https://www.bjbms.org/ojs/index.php/bjbms/article/view/10802
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