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May 1, 2024HeliyonOpen Access

Diagnostic performance of machine learning in systemic infection following percutaneous nephrolithotomy and identification of associated risk factors

Pengju Li Yiming Tang Qinsong Zeng Chengqiang Mo Nur Fahriza Mohd Ali Baohua Bai Song Ji Yubing Zhang Junhang Luo Hui Liang Rongpei Wu

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Abstract

This study aims to investigate the predictive performance of machine learning in predicting the occurrence of systemic inflammatory response syndrome (SIRS) and urosepsis after percutaneous nephrolithotomy (PCNL).

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Cite This Study

Li et al. (2024) studied this question.

synapsesocial.com/papers/68e6c1b7b6db6435876405fb https://doi.org/10.1016/j.heliyon.2024.e30956

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy2024 · 17 citations
2Machine learning constructs a model to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy2024 · 1 citations
3Machine Learning Models for Predicting Postoperative Complications and Hospitalization After Percutaneous Nephrolithotomy2025
4An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy2025 · 4 citations
5Diagnostic value of immune indicators and cytokines in sepsis after percutaneous nephrolithotomy (PCNL)2025