ArticleScientific reports2021
Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients.BMC infectious diseases · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development and internal validation of the SCAN score: a pragmatic bedside tool for early prediction of spontaneous bacterial peritonitis in patients with cirrhosis and ascites.Clinical and experimental hepatology · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Development and Temporal Validation of Explainable Machine Learning Models for Predicting Vitamin B12 Deficiency Using Routine Laboratory Analytes.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-based evaluation of prognosis in cirrhosis.Journal of translational medicine · 2024Review
- Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study.BMC gastroenterology · 2023Article
- An Improved Diagnostic of theDiagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
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.
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