Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model

Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model

Abstract Conventional models are unable to fully assess the complexity of aneurysmal subarachnoid hemorrhage (aSAH). In this study, we developed a predictive model using the extreme gradient boosting (XGBoost) algorithm to guide individualized treatment by combining inflammatory markers and clinical...

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Bibliographic Details
Main Authors: Weichong Zhou, Xingfu Liao, Hui Shi, Mingfeng Wang, Yunchong Xiao, Xilu Yu, Yilong Wu, YanYi Liu, Yin Peng, Hai Su
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Aneurysmal subarachnoid hemorrhage (aSAH)
Inflammatory markers
The XGBoost algorithm (extreme gradient boosting)
Prognosis
Predictive model
Online Access:https://doi.org/10.1038/s41598-025-05432-z
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https://doi.org/10.1038/s41598-025-05432-z

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