Epidémiologie des cancers épithéliaux de la peau [Epidemiology of epithelial skin cancers]

With no less than 15,000 estimated new cases diagnosed per year, non melanomatous carcinomas are the commonest cutaneous cancers in the Swiss population. About 1 in 3 new cancer case is a basal (BCC) or a squamous cell carcinoma (SCC). Incidence rates are steadily increasing, faster for BCC than SCC. Rates are higher for men than women and increase exponentially with age. Systematic population-based registration of non melanomatous skin cancers faces many challenges that few cancer registries can meet. Rates of these cancers in Switzerland are among the highest in Europe. Primary and secondary nationwide prevention campaigns have been carried out for nearly 20 years with a focus on the deadliest cutaneous cancer: melanoma. However, detection of non melanomatous skin cancers benefits from these campaigns since prevention messages and means of early detection are similar for melanomas and other skin cancers.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.