High performance decision support systems for multiple sclerosis treatment effectiveness in a real-world setting (R-10078)

NOT GOOD ENOUGH'. These words summarize the management of multiple sclerosis (MS) today. MS is a disease of the central nervous system. Although it remains impossible to cure MS, early and effective treatment can slow down progression. A friend of mine, Glenn, missed his window of opportunity. Therefore, he progressed from a healthy, competitive badminton player and Phd student into a unemployed patient that needs a cane to walk and feels numb from the waist down. Glenn is no exception. Finding the right treatment at the right time for the right person is extremely difficult in MS. Indeed, 14 therapies have been approved because of their efficacy in randomized controlled trials. However, because of the strict inclusion criteria for clinical trials, the effectiveness in "real-world practice" remains unknown. High performance MS specific decision support systems are required to support doctors and regulators. In this project, I will investigate whether large scale modelling of Real-World Data can improve the selection of treatments in MS. Real-World Data is defined as data derived from a number of sources that are associated with outcomes in a heterogeneous patient population in real-world settings. I believe, I can improve decision support systems by 1° developing and implementing a new modelling technique that copes better with the imperfection inherently present in real-world data sets and 2° use better and more data to train the algorithms.

Keywords:Big Data, Decision support systems,, MULTIPLE SCLEROSIS, Real-world data

Disciplines:Autoimmunity