Knowing a patient’s risk at the moment of admission to a medical unit is important for both clinical and administrative decision making: it is fundamental to carry out a health technology assessment. In this paper, we propose a non-supervised learning method based on cluster analysis and genetic algorithms to classify patients according to their admission risk. This proposal includes an innovative way to incorporate the information contained in the diagnostic hypotheses into the classification system. To assess this method, we used retrospective data of 294 patients (50 dead) admitted to two Adult Intensive Care Units (ICU) in the city of Santiago, Chile. An area calculation under the ROC curve was used to verify the accuracy of this classification. The results show that, with the proposed methodology, it is possible to obtain an ROC curve with a 0.946 area, whereas with the APACHE II system it is possible to obtain only a 0.786 area. © Springer-Verlag Berlin Heidelberg 2003.