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28 August 2023 Initiation timing prediction of fluid de-escalation for patients with sepsis using extreme gradient boosting model
Yuanyang Wang, Zunliang Wang, Sijia Zhang, Songqiao Liu
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Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272419 (2023) https://doi.org/10.1117/12.2687468
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China
Abstract
Sepsis is a serious clinical syndrome that can be life-threatening. Effective fluid resuscitation is crucial for improving the outcomes of patients with sepsis. During fluid treatment of sepsis, timely achieving a negative fluid balance is associated with better outcomes. Inappropriate fluid administration can increase risk of death in patients with sepsis. However, there is still uncertainty about the best strategy for managing fluids in sepsis. In this study, we present a machine learning model based on Extreme Gradient Boosting (XGBoost) algorithm to predict the initial timing of de-escalation phase of fluid therapy after fluid resuscitation for patients with sepsis. We collected a total of 1,353 sepsis patient records from the MIMIC-IV database for our study. From each patient, we extracted 37 hourly features that reflect their status. To assess the importance of each feature to the timing prediction, we used the SHapley additive exPlanations (SHAP) method. Based on all 37 features, our full model demonstrated a precision of 82.02%, a recall of 85.99% and a specificity of 97.03% with an area under the ROC curve (AUC) of 0.9863 well demonstrating its potential in helping clinicians determine the optimal timing for fluid de-escalation in sepsis management.
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Yuanyang Wang, Zunliang Wang, Sijia Zhang, and Songqiao Liu "Initiation timing prediction of fluid de-escalation for patients with sepsis using extreme gradient boosting model", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272419 (28 August 2023); https://doi.org/10.1117/12.2687468
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KEYWORDS
Data modeling
Machine learning
Education and training
Decision making
Performance modeling
Medicine
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