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Utilizing a large ICU cohort with detailed clinical information, we identified ACLF patients with two or more OFs according to the EASL-CLIF and NACSELD definitions. ML model was developed for each definition to predict 30-day mortality. The Shapley value was estimated to explain the models. Validation and calibration of these models were performed.Results: Of 5,994 patients with cirrhosis admitted to ICU, 1,511 met NACSELD criteria, and 1,692 met EASL-CLIF grade II or higher criteria. The CatBoost ACLF (CBA) model had the greatest accuracy in the NACSELD cohort (area under curve [AUC] of 0.87), while the Random Forest ACLF (RFA) model performed best in the EASL-CLIF cohort (AUC of 0.83). Both models showed robust calibration. The models were explained by SHAP score analysis, yielding a rank list, and the top twelve predictors were selected. Both simplified models demonstrated similar performance (CBA model: AUC 0.89, RFA model: AUC 0.81) and significantly outperformed contemporary scoring systems, including CLIF-C ACLF and MELD 3.0. The models were validated in both internal and external cohorts. A simple-to-use online tool was created to predict mortality rates.Conclusions: We presented explainable, well-validated, and calibrated predictive models for ACLF patients with two or more OFs, which outperformed existing predictive scores.<\/jats:p>","DOI":"10.3350\/cmh.2025.0573","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:40:09Z","timestamp":1756773609000},"page":"1355-1371","update-policy":"https:\/\/doi.org\/10.3350\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures"],"prefix":"10.3350","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2703-5954","authenticated-orcid":true,"given":"Yee 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