Leveraging environmental information for enhanced prediction of cardiac readmissions

Accurately predicting readmission risk for heart failure patients is a hot topic in the field of survival analysis and healthcare. Current models show room for improvement. This study aims to explore the ability of environmental data to improve prediction accuracy. We conducted a retrospective analysis, integrating environmental factors with clinical data. Experiments show that our combined model achieved a 37% higher C-index than the best baseline. Further tests confirmed that adding any single environmental feature consistently boosted the performance of all baseline models. In addition, environmental data alone have clear limitations. Models using only these variables performed significantly worse than those incorporating clinical features. This indicates that environmental factors are powerful supplements, not replacements, for established clinical predictors. In conclusion, our findings provide strong evidence that environmental information serves as a valuable and complementary tool, significantly improving the accuracy of heart failure readmission-risk prediction when used alongside clinical data.