ArticleFrontiers in cardiovascular medicine2023
Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning-based risk classification of depressive symptoms among patients with hearing loss: evidence from the Health and Retirement Study (HRS).Comprehensive psychoneuroendocrinology · 2026Article
- Comparative performance of manual triage systems and ML models in predicting clinical outcomes among patients transported to the emergency department by emergency medical services.BMC emergency medicine · 2026Article
- A Machine Learning-Based Clinical Decision Support Tool for Intertrochanteric Hip Fracture Patients to Predict Postoperative Anemia Risk: A Retrospective Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Machine Learning for Classification in Lung Cancer Using Routine Clinical and Laboratory Data.Annals of surgical oncology · 2026Article
- Article
- Real-time monitoring of in-hospital mortality risk in intensive care units heart failure patients using an extreme gradient boosting model.Journal of geriatric cardiology : JGC · 2026Article
- Stress hyperglycemia ratio in risk stratification and treatment optimization for patients on invasive mechanical ventilation: a secondary analysis of the MIMIC-IV database.BMC infectious diseases · 2026Article
- Multimodal Transformer-Based Electrocardiogram Analysis for Cardiovascular Comorbidity Detection: Model Development and Validation Study.JMIR formative research · 2026Article
- Public health risk stratification using hybrid machine learning: a reproducible analysis of performance, stability, and risk attribution.Frontiers in bioinformatics · 2026Article
- Enhancing clinically cardiovascular machine learning model for risk prediction via sample augmentation.Frontiers in medicine · 2026Article
- From Logistic Regression to Foundation Models: Factors Associated With Improved Forecasts.Cureus · 2025Review
- Independent prognostic importance of blood urea nitrogen to albumin ratio in critically ill patients with congestive heart failure.Scientific reports · 2025Article
- Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Development, Validation, and Deployment of a Time-Dependent Machine Learning Model for Predicting One-Year Mortality Risk in Critically Ill Patients with Heart Failure.Bioengineering (Basel, Switzerland) · 2025Article
- Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.Journal of clinical medicine · 2025Article
- Prediction of in-hospital death among patients admitted to a tertiary care hospital over the first 10 years: a machine learning approach.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Risk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF. Methods: eXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model ( Results: The total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757-0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%-90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app). Conclusion: This study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.