ArticleJournal of translational medicine2022
A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis.Clinical cardiology · 2025Pooled it
- Development and External Validation of an Explainable Machine-Learning Model for Predicting Postoperative Pulmonary Complications in Older Adults Undergoing Degenerative Spine Surgery.Journal of clinical medicine · 2026Article
- Review
- Interpretable machine learning for identifying co-occurring internet addiction and maladaptive cognitive emotion regulation in older adults: a cross-sectional study.Frontiers in public health · 2026Article
- Machine Learning and Abdominal Aortic Aneurysm: A New Paradigm in Prediction and Prognosis after Endovascular Aneurysm Repair.Annals of vascular diseases · 2026Review
- Triglyceride-glucose index trajectories and in-hospital mortality in heart failure: a retrospective cohort study.Frontiers in cardiovascular medicine · 2026Article
- Development of machine learning-based predictive models for fertility intentions in patients with Crohn's disease.Frontiers in reproductive health · 2026Article
- Identifying subjective life expectancy risk factors in physically active and inactive middle-aged and older adults using machine learning models.BMC public health · 2025Article
- Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.BMC psychiatry · 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 machine learning-based severity stratification tool for high altitude pulmonary edema.BMC medical informatics and decision making · 2025Article
- An explainable multi-objective hybrid machine learning model for reducing heart failure mortality.PeerJ. Computer science · 2025Article
- Development and validation of a nomogram prediction model for factors influencingFrontiers in endocrinology · 2025Article
- Temporal variations in and predictive values of ABG results prior to in-hospital cardiac arrest.Journal of medicine, surgery, and public health · 2024Article
- Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.Cureus · 2024Review
- The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support.ESC heart failure · 2024Review
- Interpretable mortality prediction model for ICU patients with pneumonia: using shapley additive explanation method.BMC pulmonary medicine · 2024Article
- Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.Journal of translational medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.
methodsUsing a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.
resultsThe performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk.
conclusionUsing readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
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