In this SERIE dedicated to Artificial intelligence, the idea is to start from studies where there is a certain amount of information that is used to reach conclusions. In particular, information from bioinformatics, metagenomics, it can also be looking for mutations, can certain mutational profiles imply or impact vulnerability.
The interest is both in the very technical aspects, how machine learning works, automatic learning. More importantly, what is the use of when studying health trajectories, in identifying these different trajectories, tools, extremely powerful algorithms to identify subjects at risk, with more complex trajectories, and also to predict what will happen next, in order to be able to develop specific care or support for these groups.
« Artificial intelligence, when it is applied to this issue of health inequalities, is a fabulous tool, since it helps us to better identify and better support the subjects.» Etienne Audureau
« This SERIE will allow students to discover the basics of artificial intelligence but above all to discover a whole range of theoretical but practical application in the clinic of these artificial intelligence technologies and to really see that all these models of which we speak about machine -learning, supervised learning allow concrete applications that already improve the care of patients with solutions that are marketed. It is a practical approach through examples of real clinical applications. » Sébastien Mule
« We will tackle the nutritional aspect in relation to the intestinal microbiota involved in vulnerable populations, their impacts on the microbiota, and on the health of the individual. The nutritional aspect, the nutrition, digestion - microbiota relationship and how bioinformatics makes it possible to quantify, or qualify the intestinal microbiota. This microbiota is known to have an impact on people's health. Students will learn how to quantify a microbiota in order to understand the impact of nutrition on this microbiota, and how this microbiota can be associated with certain diseases. This quantification uses computer tools. » Denis Mestivier