Filipe Afonso

Classification and Regression Trees on Aggregate Data Modeling: An Application in Acute Myocardial Infarction

Cardiologists are interested in determining whether the type of hospital pathway followed by a patient is predictive of survival. The study objective was to determine whether accounting for hospital pathways in the selection of prognostic factors of one-year survival after acute myocardial infarction AMI provided a more informative analysis than that obtained by the use of a standard regression tree analysis CART method . Information on AMI was collected for 1095 hospitalized patients over an 18-month period. The construction of pathways followed by patients produced symbolic-valued observations requiring a symbolic regression tree analysis. This analysis was compared with the standard CART analysis using patients as statistical units described by standard data selected TIMI score as the primary predictor variable. For the 1011 84, resp. patients with a lower higher TIMI score, the pathway variable did not appear as a diagnostic variable until the third second stage of the tree construction. For an ecological analysis, again TIMI score was the first predictor variable. However, in a symbolic regression tree analysis using hospital pathways as statistical units, the type of pathway followed was the key predictor variable, showing in particular that pathways involving early admission to cardiology units produced high one-year survival rates.

From the Symbolic Analysis of Virtual Faces to a Smiles Machine

In this paper, we present an application of symbolic data processing for the design of virtual characters smiling facial expressions. A collected database of virtual characters smiles directly created by users has been explored using symbolic data analysis methods. An unsupervised analysis has enabled us to identify the morphological and dynamic characteristics of different types of smiles as well as of combinations of smiles. Based on the symbolic data analysis, to generate different smiling faces, we have developed procedures to automatically reconstitute smiling virtual faces from a point in a multidimensional space corresponding to a principal component analysis plane.

Strategies evaluation in environmental conditions by symbolic data analysis: application in medicine and epidemiology to trachoma

Trachoma, caused by repeated ocular infections with Chlamydia trachomatis whose vector is a fly, is an important cause of blindness in the world. We are presenting here an application of the Symbolic Data Analysis approach to an interventional study on trachoma conducted in Mali. This study was conducted to choose among three antibiotic strategies those with the best cost-effectiveness ratio and to find the demographic and environmental parameters on which we could try to intervene. The Symbolic Data Analysis approach aims at studying classes of individuals considered as new units. These units are described by variables whose values express for each class the variation of the values taken by each of its individuals. Finally, the results obtained are compared to those previously provided by multiple logistic regression analysis. Symbolic Data Analysis actually provides a new perspective on this study and suggests that some demographic, economics and environmental parameters are related to the disease and its evolution during the treatment, whatever the strategy. Moreover, it is shown that the efficiency of each strategy depends on environmental parameters.