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.

Epidemiology of pulmonary hypertension: new data from the Swiss registry.

since 1999 data from pulmonary hypertension (PH) patients from all PH centres in Switzerland were prospectively collected. We analyse the epidemiological aspects of these data. PH was defined as a mean pulmonary artery pressure of >25 mm Hg at rest or >30 mm Hg during exercise. Patients with pulmonary arterial hypertension (PAH), PH associated with lung diseases, PH due to chronic thrombotic and/or embolic disease (CTEPH), or PH due to miscellaneous disorders were registered. Data from adult patients included between January 1999 and December 2004 were analysed. 250 patients were registered (age 58 +/- 16 years, 104 (41%) males). 152 patients (61%) had PAH, 73 (29%) had CTEPH and 18 (7%) had PH associated with lung disease. Patients <50 years (32%) were more likely to have PAH than patients >50 years (76% vs. 53%, p <0.005). Twenty-four patients (10%) were lost to followup, 58 patients (26%) died and 150 (66%) survived without transplantation or thrombendarterectomy. Survivors differed from patients who died in the baseline six-minute walking distance (400 m [300-459] vs. 273 m [174-415]), the functional impairment (NYHA class III/IV 86% vs. 98%), mixed venous saturation (63% [57-68] vs. 56% [50-61]) and right atrial pressure (7 mm Hg [4-11] vs. 11 mm Hg [4-18]). PH is a disease affecting adults of all ages. The management of these patients in specialised centres guarantees a high quality of care. Analysis of the registry data could be an instrument for quality control and might help identify weak points in assessment and treatment of these patients.