Heart failure (HF) is a worldwide major cause of mortality and morbidity for which many predictive scores have been defined. Selecting which explanatory variables to include in a given score is a common difficulty, as a balance must be found between statistical fit and practical application. This article presents a methodology for constructing parsimonious event scores combining a stepwise selection of variables with ensemble scores obtained by aggregation of several scores, using several classifiers, bootstrap samples and various modalities of random selection of variables. The stepwise selection allows constructing a succession of scores, with the practitioner able to choose which score best fits his needs. The methods proposed herein can be reproduced on any set of variables as long as the training dataset comprises a sufficient number of cases. Three methods were compared in an application to construct parsimonious short-term scores in chronic HF patients, two involving a backward selection of the variables based on their coefficients in an ensemble score and the third involving a forward selection of the variables maximizing the AUC. The working sample consisted of 11,411 (patient, visit) couples from the GISSI-HF database, with 5,595 events (duplicated in order to balance the sample) and 5,816 non-events. Sixty-two candidate explanatory variables were studied. The outcome was the composite endpoint of death or hospitalization for worsening HF within 180 days of a visit. The three methods yielded a selection of 50, 59 and 26 variables, respectively. For a given number of selected variables, most were common to the three methods. Focusing on the fastest method, four scores were constructed, yielding out-of-bag AUCs ranging from 0.81 (26 variables) to 0.76 (2 variables). These results are slightly better than those obtained by other scores reported in the literature using a similar number of variables.