Developing and optimizing machine learning algorithms for predicting in-hospital patient charges for Congestive Heart Failure Exacerbations, Chronic Obstructive Pulmonary Disease Exacerbations and Diabetic Ketoacidosis

BackgroundHospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. ObjectiveThe purpose of this study is to predict in-hospital charges for each condition using Machine Learning (ML) models. MethodsWe conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We used numerous ML techniques to predict in-hospital total cost. ResultsWe found that linear regression (LM), gradient boosting (GBM) and extreme gradient boosting (XGB) models had good predictive performance and were statistically equivalent, with training R-Squared values ranging from 0.49-0.95 for CHF; 0.56-0.95 for COPD; and 0.32-0.99 for DKA. We identified important key features driving costs, including patient age, length-of-stay, number of procedures. and elective/non-elective admission. ConclusionsML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.