ArticleWorld journal of emergency surgery : WJES2025
An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study.
Article in World journal of emergency surgery : WJES, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Preoperative prediction of acute severe cholecystitis using an attentive interpretable tabular network (TabNet)-based radiomics model and a stacking ensemble: a two-center study.Annals of medicine · 2026Article
- Explainable early prediction of short-term severe deterioration in gallstone-related biliary disease using MIMIC-IV, eICU, and local real-world data.BMC gastroenterology · 2026Article
- Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases.Langenbeck's archives of surgery · 2026Article
- An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Advances in Imaging and Diagnosis of Emphysematous Cholecystitis.Healthcare (Basel, Switzerland) · 2026Review
- An explainable predictive machine learning model of osteopenia for perimenopausal women based on clinical data: a retrospective single-center study.Frontiers in endocrinology · 2026Article
- Development and external validation of a machine learning model for predicting gangrenous cholecystitis.Frontiers in medicine · 2026Article
- Artificial intelligence in gangrenous cholecystitis: advances in risk factor identification and preoperative prediction-a scoping review.Frontiers in surgery · 2026Review
- Construction of a Preoperative Prediction Model for TACE Resistance in Primary Hepatocellular Carcinoma Based on Machine Learning Algorithms.Journal of hepatocellular carcinoma · 2026Article
- Development and Validation of an Explainable Machine Learning Model for Prediction of Massive Transfusion in Upper Gastrointestinal Bleeding.Risk management and healthcare policy · 2026Article
- Development and validation of machine learning tools for predicting postoperative complications in acute calculous cholecystitis.Updates in surgery · 2025Article
- Construction of a predictive model for the risk of moderate-to-severe cancer-related fatigue in colorectal cancer chemotherapy patients: an interpretable machine learning approach.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study.World journal of emergency surgery : WJES · 2025Article
- Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.Scientific reports · 2025Article
- Development and Validation of an Explainable Machine Learning Model for Gangrenous Cholecystitis Prediction: A Multicenter Retrospective Study.Journal of inflammation research · 2025Article
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- Metaheuristic-based gallstone classification using rotational forest explained with SHAP.Frontiers in digital health · 2025Article
- Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer.Frontiers in cardiovascular medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundGangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an explainable and cost-effective predictive model for GC utilizing ML and Shapley Additive explanation (SHAP) algorithm.
resultsThis study included a total of 1006 patients with 26 clinical features. Through 5-fold CV, the best performing integrated learning model, XGBoost, was identified. The model was interpreted using SHAP to derive the feature subsets WBC, NLR, D-dimer, Gallbladder width, Fibrinogen, Gallbladder wallness, Hypokalemia or hyponatremia, these subsets comprised the final diagnostic prediction model.
conclusionsThe study developed a explainable predictive tool for GC at an early stage. This could assist doctors to make quick surgical intervention decisions and perform surgery on patients with GC as soon as possible.
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