ArticleCancers2023
Machine Learning-Based Mortality Prediction Model for Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU).
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Measuring Uncertainty to Inform Time-Limited Trials: Do We Know What We Don't Know?Critical care medicine · 2026Article
- Construction and validation of a prognostic prediction model for critically ill lung cancer patients based on respiratory functional reserve and systemic inflammatory characteristics.American journal of cancer research · 2026Article
- Mortality Prediction from Patient's First Day PAAC Radiograph in Internal Medicine Intensive Care Unit Using Artificial Intelligence Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Review
- Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.BMC medical informatics and decision making · 2025Article
- The role of diet in cancer: the potential of shaping public policy and clinical outcomes in the UK.Genes & nutrition · 2024Review
- Article
- Interpretable early mortality prediction in oncology ICU patients: A dual-cohort validation of a LASSO-XGBoost-SHAP framework.Digital healthArticle
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14 authors.
Funding
Abstract
backgroundAlthough cancer patients are increasingly admitted to the intensive care unit (ICU) for cancer- or treatment-related complications, improved mortality prediction remains a big challenge. This study describes a new ML-based mortality prediction model for critically ill cancer patients admitted to ICU. PATIENTS AND
methodsWe developed CanICU, a machine learning-based 28-day mortality prediction model for adult cancer patients admitted to ICU from Medical Information Mart for Intensive Care (MIMIC) database in the USA (
resultsA total of 6900 patients were included, with a 28-day mortality of 10.2%/12.7%/36.6% and a 1-year mortality of 30.0%/36.6%/58.5% in the YCC, SMC, and MIMIC-III cohort. Nine clinical and laboratory factors were used to construct the classifier using a random forest machine-learning algorithm. CanICU had 96% sensitivity/73% specificity with the area under the receiver operating characteristic (AUROC) of 0.94 for 28-day, showing better performance than current prognostic models, including the Acute Physiology and Chronic Health Evaluation (APACHE) or Sequential Organ Failure Assessment (SOFA) score. Application of CanICU in two external data sets across the countries yielded 79-89% sensitivity, 58-59% specificity, and 0.75-0.78 AUROC for 28-day mortality. The CanICU score was also correlated with one-year mortality with 88-93% specificity.
conclusionCanICU offers improved performance for predicting mortality in critically ill cancer patients admitted to ICU. A user-friendly online implementation is available and should be valuable for better mortality risk stratification to allocate ICU care for cancer patients.
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