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Sparse longitudinal modeling using matrix factorization



Lukasz Kidzinski

Stanford University


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