Abstract

Vasopressors administration in intensive care units is a risky surgical procedure that can be associated with infections, especially if done urgently such as in the case of unexpected systemic shock. The early prediction of a patient's transition to vasopressor dependence could improve overall outcomes associated with the procedure. Personalized medicine in the ICU encompasses the customization of healthcare on the level of individual patients, with diagnostic tests, monitoring interventions and treatments being fitted to the individual rather than the "average" patient. In this scope, this paper proposes an ensemble fuzzy modeling approach to a classification problem based on subgroups of patients identified by individual characteristics. A fuzzy c-means clustering algorithm was implemented to find subgroups of patients and each subgroup was used to develop a fuzzy model. The final classification of the ensemble fuzzy approach is obtained using two output selection criteria: an *a priori* decision criterion based on the distance from the cluster centers to the patients' characteristics, and an *a posteriori* decision criterion based on the uncertainty of the model output. The performance of the proposed approach is investigated using a real world clinical database and nine benchmark datasets. The ensemble fuzzy model approach performs better than the single model for the prediction of vasopressors administration in the ICU, being the *a posteriori* approach the best performer, with an average AUC of 0.85, showing this way the advantage of a personalized approach for patient care in the ICU.