Predicting heart failure emergency readmissions

Abstract: Recent progress in treatment interventions has resulted in increased survival rates and longevity for diagnosed heart failure patients. However, heart failure still remains one of the leading causes of rehospitalization worldwide, where emergency readmissions continue to be a common occurrence. The multifactorial complexity of heart failure makes clinical judgment difficult and may lead to erroneous discharge prognoses and estimates in recovery trajectories. Recognizing emergency readmissions among heart failure patients who have been discharged is crucial within the critical six-month post-discharge period to proactively address additional support needs. To address the research question, “To what extent can machine learning models predict emergency readmissions in Chinese heart failure patients within six months post-discharge?”, this paper uses electronic health records obtained from a single healthcare center in China, containing 2,008 validated heart failure patients. This study adopts an experimental research methodology, where four machine learning models are developed to explore the research question. To ensure robustness, 10-fold cross-validation with stratified sampling and a two-step feature selection process is performed in addition to evaluation through metrics such as the area under the receiving operating curve and F1 Score. The findings indicate only modest predictive capability among the classifiers in the validation cohort. The best-achieved area under the receiving operating curve and F1 Score are obtained from separate classifiers with scores of 0.682 and 0.577, respectively. The findings provide valuable insights into future research on the effectiveness of ML-based prediction models for emergency readmission in Chinese heart failure patients.