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- W3208574002 abstract "Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model's outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783-0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784-0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive variables were total protein, C-reactive protein, prothrombin activity, cholinesterase, lymphocyte ratio and apolipoprotein A1. For binary classifier, the sensitivity and NPV of MODEL-1 were 0.894 and 0.885, respectively, while for MODEL-2 they were 0.927 and 0.904, respectively. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. We also identified 6 important variables closely related to the occurrence of SBP." @default.
- W3208574002 created "2021-11-08" @default.
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- W3208574002 date "2021-11-04" @default.
- W3208574002 modified "2023-10-02" @default.
- W3208574002 title "Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites" @default.
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- W3208574002 doi "https://doi.org/10.1038/s41598-021-00218-5" @default.
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