1 research outputs found
Modeling treatment events in disease progression
Ability to quantify and predict progression of a disease is fundamental for
selecting an appropriate treatment. Many clinical metrics cannot be acquired
frequently either because of their cost (e.g. MRI, gait analysis) or because
they are inconvenient or harmful to a patient (e.g. biopsy, x-ray). In such
scenarios, in order to estimate individual trajectories of disease progression,
it is advantageous to leverage similarities between patients, i.e. the
covariance of trajectories, and find a latent representation of progression.
Most of existing methods for estimating trajectories do not account for events
in-between observations, what dramatically decreases their adequacy for
clinical practice. In this study, we develop a machine learning framework named
Coordinatewise-Soft-Impute (CSI) for analyzing disease progression from sparse
observations in the presence of confounding events. CSI is guaranteed to
converge to the global minimum of the corresponding optimization problem.
Experimental results also demonstrates the effectiveness of CSI using both
simulated and real dataset