Table 1_Construction of a readmission prediction model and development of a decision support system for chronic heart failure patients based on patient-reported outcome measures.docx

Model performance and interpretability were evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), Decision Curve Analysis (DCA), and SHapley Additive exPlanations (SHAP). A visualized decision-making tool was implemented on the MATLAB platform.</p>Results<p>Testing set showed the AutoML model achieved significantly enhanced predictive efficacy [AUC = 0.8909, 95% Confidence Interval (CI): 0.8392 to 0.9426].

SHAP analysis revealed key multidimensional factors influencing prognosis, among which milder depressive symptoms, higher levels of social support, and favorable appetite-sleep status were important protective factors reducing readmission risk, while female sex and advanced age were identified as risk predictors. Decision curve analysis further confirmed that applying this model yielded positive net clinical benefit across a wide risk threshold range of 1.2% to 78.6%.</p>Conclusion<p>This study achieved efficient translation of PROM data into clinical decisions and innovatively demonstrated the independent predictive value of psychosocial factors for readmission.

The developed CDSS system provides a feasible pathway for the proactive prevention and management of heart failure.</p>