Fingolimod real world experience

Fingolimod is a Multiple Sclerosis treatment licensed in Europe since 2011. Its efficacy has been demonstrated in three large phase III trials, used in the regulatory submissions throughout the world. As usual, in these trials the inclusion and exclusion criteria were designed to obtain a homogeneous population, with interchangeable characteristics in the different treatment arms. Although this is the best strategy to achieve a robust answer to the investigation question, it does not guaranty the treatment efficacy in the clinical practice, since in the real world there are concomitant treatments, comorbidities, adherence and persistence challenges. But, to make informed treatment decision for a real life patient, we need to have evidence of the treatment efficacy, what has been called treatment effectiveness. This work aims to review fingolimod effectiveness, using as source of information abstracts, posters and manuscripts. This unorthodox strategy was developed because more than half of the published experience with fingolimod is still on abstracts and posters. Only a small part of the studies reviewed are already published in peer reviewed journals. Fingolimod seems to be, at least, as effective and safe as it was on clinical trials, and with its long term experience no new safety signals were observed. In the Portuguese hospital perspective, early treatment with fingolimod is expected to result in better clinical outcomes associated with a more efficient healthcare resources allocation.

Scalable semantic aware context storage

The number of connected devices collecting and distributing real-world information through various systems, is expected to soar in the coming years. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, but none of them have been widely adopted. In previous work we addressed this challenge, however our solution had some drawbacks: poor semantic extraction and scalability. In this paper we discuss ways to efficiently deal with representation schemes' diversity and propose a novel d-dimension organization model. Our evaluation shows that d-dimension model improves scalability and semantic extraction.

Semantic features for context organization

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.