Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods

Abstract

We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally found to be important predictors of hospitalization. Utilizing prediction models to identify those who are prone to hospitalization could be useful in developing early interventions to improve outcomes among HCBS users.

Keywords: Medicaid; Medicare; home- and community-based care; hospitalization; long-term care; machine learning.

Figure 1.

Performance of algorithms for predicting hospitalization in August with test set based on area under precision-recall curve (a) and area under receiver operating characteristic curve (b). Note. AUPRC = Area Under Precision-Recall Curve; AUROC = Area Under Receiver Operating Characteristic Curve; TPR = True Positive Ratio; FPR = False Positive Ratio. *Square dots indicate the precision and recall based on the highest F-1 value for each algorithm.

Figure 2.

Feature importance by using random forest and XGBoost. Note. COPD = Chronic Obstructive Pulmonary Disease; RA OA = Rheumatoid Arthritis/Osteoarthritis; CHF = Chronic Heart Failure; LOS = Length of stay during previous hospitalizations. *Feature importance values of LOS_m5 and LOS_m6 from the XGB model are 0.21 and 0.19, respectively.