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Diseases like cancer are caused by a diversity of different factors interacting together, whose study requires a huge amount of data. Compiling this data is an expensive and time-consuming task that can be carried out in an easier, faster and more secure way with the support of Information and Communication Technologies (ICT). Nevertheless, the majority of epidemiologic studies are executed without this support of informatics or with basic tools that are developed by unqualified professionals. As a consequence, the integrity of the collected data cannot be assured, and the reliability of the studies is usually decreased. This work presents an ontology-based Information System for the development of multicenter epidemiologic studies on cancer that allows 1) collecting, storing and editing medical data from different hospitals and 2) reusing the compiled data by means of their integration with data from other systems. This system has been satisfactorily applied to an epidemiologic study of colorectal cancer in Galicia, Spain.

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.