OBJECTIVES: Real-world big data accessible through electronic health record (EHR) systems offer opportunities to collect generalizable information to populate economic models. Using a supervised machine learning approach, the objectives were: (a) to mine a hospital EHR for transition probabilities of high-risk patients for developing hospital-acquired pressure ulcers (HAPUs); and (b) to compare efficiency and accuracy of predictive methods between Markov modeling and Bayesian inference with EHR data. METHODS: This study used a de-identified panel of patient hospitalizations since 2010 in a U.S. tertiary academic medical center EHR to study Braden scores of patient risk for developing HAPUs. The study focused on patients hospitalized for ≥5 days and at least two Braden scores. Braden scores were converted from an ordered scale into five categories (i.e. minimal risk; at risk; moderate risk; high risk; very high risk). A 10-stage Markov model was constructed via supervised machine learning using R software designating the five Braden categories as transition states, as well as end-states for discharge or HAPU incidence. Results of the Markov approach were age-adjusted and compared to prior probabilities of HAPU risk derived from naïve and full Bayesian inference. Measures of computational accuracy and efficiency were derived to compare analytical approaches. RESULTS: The EHR provided a panel of over 34,787 patients. The Markov model yielded transition probabilities for each of 7 transitions. Patient risk for developing a HAPU is highly predictable after approximately 4-6 iterations. The very high-risk cohort had a clinically meaningful increase in risk for HAPU development of 2.35% compared to a minimal risk transition probability of 0.05% (p<0.001). Neither of the Bayesian classifiers provided accurate comparisons. CONCLUSIONS: Real-world big data from an EHR enables outcomes researchers to mine transition probabilities using supervised machine learning. These results can be obtained to efficiently populate Markov models for cost-effectiveness and decision analysis.