A common problem in clinical practice is to predict disease progression from sparse observations of individual patients. The classical approach to modeling this kind of data relies on a mixed-effect model where time is considered as both a fixed effect (a population trajectory) and a random effect (an individual trajectory).
In our work, we map the problem to a matrix completion framework and solve it using matrix factorization techniques. The proposed approach does not require assumptions of the mixed-effect model and it can be naturally extended to multivariate measurements.
Date : Lundi le 23 avril 2018
Heure : 10h30 à 11h30
Lieu : Pavillon André-Aisenstadt
Salle : 5340