Abstract: Electronic health records (EHR) provide opportunities to leverage vast arrays of data to help prevent adverse events, improve patient outcomes, and reduce hospital costs. A postoperative complications prediction system is provided that extracts data from the EHR and creates features. An analytic engine then provides model accuracy, calibration, feature ranking, and personalized feature responses. The system allows clinicians to interpret the likelihood of an adverse event occurring, general causes for these events, and the contributing factors for each specific patient.