Studies in the last decade have focused on identifying patients at risk of readmission using predictive models, in an objective to decrease costs to the healthcare system. However, real-time models specifically identifying readmissions related to hospital adverse-events are still to be elaborated. A supervised learning approach was adopted using different machine learning algorithms based on features available directly from the hospital information system and on a validated dataset elaborated by a multidisciplinary expert consensus panel. Accuracy results upon testing were in line with comparable studies, and variable across algorithms, with the highest prediction given by Artificial Neuron Networks. Features importances relative to the prediction were identified, in order to provide better representation and interpretation of results. Such a model can pave the way to predictive models for readmissions related to patient harm, the establishment of a learning platform for clinical quality measurement and improvement, and in some cases for an improved clinical management of readmitted patients.