• decision support system
• machine learning
• population health management

Population aging and the increase of chronic conditions incidence and prevalence produce a higher risk of hospitalization or death. This is particularly high for patients with multimorbidity, who usually receive ineffective and inefficient treatments, leading to a great consumption of resources. Identifying as soon as possible high-risk patients becomes an important challenge to improve health care service provision and to reduce costs. Nowadays, population health management, intended as the risk assessment process for dening patients cohorts and stratifying members by the risk of preventable hospitalizations or death, can be used to identify these "complex" patients. Thanks to the growing computational power, it can exploit machine learning algorithms, intelligent models which learn from experience and have the capability of analysing huge amount of data. The aim of this study is to validate machine learning algorithms (Naive Bayes, CART, C5.0, Conditional Inference Tree, Random Forest, Articial Neural Network, LASSO and some combinations of them) to predict the risk of hospitalization or death and to identify high-risk patients, starting from administrative and/or socio-economic data on the residents in the Local Health Unit of Central Tuscany. The high sensitivity, always greater than 70%, points out that a great part of the target patients is correctly identied by the models, leading to the improving of the quality of their lives, and that not many chronic patients having really need of specic treatments are misclassied. The models, at last, have a very good discriminatory power, outperforming the methods currently used in Tuscany for the identication of high-risk patients (6 vs 40 for the Positive Predictive Ratio metric, which underlines the goodness of the new classiers). Because of these considerations, the models could be really employed for the selection of the complex patients, to support the general practitioners' decisions and to reduce the risk of hospitalization or death.