Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values
Abstract This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and provide valuable guidance for urologists i...
Saved in:
Main Authors: | Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-88704-y |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture
by: Lefteris Benos, et al.
Published: (2025-01-01) -
Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning
by: Fei Wang, et al.
Published: (2025-01-01) -
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
by: Ahmed M. Salih, et al.
Published: (2025-01-01) -
Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosisResearch in context
by: Caihong Ning, et al.
Published: (2025-02-01) -
Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke
by: Lulu Zhang, et al.
Published: (2025-02-01)