Random Forests on Ubiquitous Data for Heart Failure 30-Day Readmissions Prediction

Heart failure is the most common reason for unplanned hospital readmissions. Typical 30 day readmission prediction models either use data that are not readily available at the majority of US hospitals or use modeling techniques that do not provide adequate prediction accuracy. Moreover, the tendency of ongoing studies is to incorporate clinical data that is only present in the most modern electronic health record systems (EHRs). This is problematic as the population most affected by heart disease, the rural poor, is also the same population whose hospitals have the slowest adoption rates of advanced EHR systems. We apply the machine learning technique random forests to administrative claims data to predict unplanned all-cause 30 day readmissions for congestive heart failure patients in a hospital system located in central Virginia, USA. We form two random forests model variants based on datasets comprised of procedure data, diagnosis data, a combination of both, and basic demographic data. Our results show significant predictive performance, yield importance rankings for candidate variables, and address heart failure readmissions in high-need areas.