An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac.

The cesarean section (CS) rates are important global indicators for measuring the access to obstetric services. In 2001, Robson proposed a CS classification in ten-groups as the most appropriate to compare surgery rates. However, having a decisional support system from Robson's Classification is enough to reduce CS rates? The births analysis that occurred in 2016, inside a public hospital maternity, showed 1,946 deliveries of which 35.7% were CS with a positive growth trend (R2 = 0.137).

This work focusses on our integration steps of complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated facetted search tool, followed by information visualisation based on automatic information extraction results from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals.