Key result
Machine learning models show promise for improving early HF diagnosis, readmission risk, and mortality prediction.
Why the study?
Heart failure is a leading cause of death with prohibitive treatment costs, and early prediction of risk, readmissions, and mortality could reduce costs and optimize hospital management.
Do predictive analytics models accurately predict heart failure risk, readmission, and mortality?
Do predictive analytics models accurately predict heart failure risk, readmission, and mortality?
Machine learning and predictive analytics show promise in heart failure risk stratification and outcome prediction, but require further benchmarking before clinical adoption.
May aid HF risk stratification; leaves open prospective validation before clinical adoption.
Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved readmission prediction would also greatly help hospitals, allowing them to manage their treatment programs and budgets better. This literature review aims to summarize recent studies of predictive analytics models that have been constructed to predict heart failure risk, readmission, and mortality. Random forest, logistic regression, neural networks, and XGBoost were among the most common modeling techniques applied. Most selected studies leveraged structured electronic health record data, including demographics, clinical values, lifestyle, and comorbidities, with some incorporating unstructured clinical notes. Preprocessing through imputation and feature selection were frequently employed in building the predictive analytics models. The reviewed studies exhibit demonstrated promise for predictive analytics in improving early heart failure diagnosis, readmission risk stratification, and mortality prediction. This review study highlights rising research activities and the potential of predictive analytics, especially the implementation of machine learning, in advancing heart failure outcomes. Further rigorous, comprehensive syntheses and head-to-head benchmarking of predictive models are needed to derive robust evidence for clinical adoption.
No takes yet. Share an insight, caveat, or question.
Hidayaturrohman et al. (2024) conducted a review in Heart failure. Machine learning-based predictive analytics models vs. Traditional statistical approaches or other models was evaluated on Prediction performance (Accuracy, AUC, etc.) for heart failure diagnosis, readmission, and mortality. Machine learning-based predictive analytics models, including random forest and XGBoost, demonstrated promise in improving early heart failure diagnosis, readmission risk stratification, and mortality prediction.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: