A stacking machine learning model using 166 clinical biomarkers effectively predicted emergency readmissions in discharged heart failure patients, achieving an accuracy of 89.41% and an AUC of 0.881.
Does a stacking machine learning model accurately predict emergency readmissions in discharged heart failure patients?
A stacking machine learning model using EHR data can accurately predict emergency readmissions in heart failure patients, potentially allowing for proactive interventions.
Effect estimate: AUC 0.881
Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the predictions of the three best-performing models for final classification. The stacking ML model provided an accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC) of 89.41%, 90.10%, 89.41%, 87.83%, 89.28%, and 0.881, respectively. This indicates the effectiveness of the proposed model in predicting emergency readmissions. The healthcare providers can intervene pro-actively to reduce emergency hospital readmission risk and improve patient outcomes and decrease healthcare costs using the proposed model.
Rahman et al. (Fri,) conducted a other in Heart failure (n=2,008). Stacking machine learning model vs. Classical machine learning models was evaluated on Emergency readmission (AUC 0.881). A stacking machine learning model using 166 clinical biomarkers effectively predicted emergency readmissions in discharged heart failure patients, achieving an accuracy of 89.41% and an AUC of 0.881.