A Multi-Task Learning Approach to Personalized Progression Modeling

Mohamed Ghalwash, Daby Sow

Abstract: Modeling disease progression is an active area of research. Many computational methods for progression modeling have been developed but mostly at population levels. In this paper, we formulate a personalized disease progression modeling problem as a multi-task regression problem where the estimation of progression scores at different time points is defined as a learning task. We introduce a Personalized Progression Modeling (PPM) scheme as a novel way to estimate personalized trajectories of disease by jointly discovering clusters of similar patients while estimating disease progression scores. The approach is formulated as an optimization problem that can be solved using existing optimization techniques. We present efficient algorithms for the PPM scheme, together with experimental results on both synthetic and real world healthcare data proving its analytical efficacy over other 4 baseline methods representing the current state of the art. On synthetic data, we showed that our algorithm achieves over 40% accuracy improvement over all the baselines. On the healthcare application PPM has a 4% accuracy improvement on average over the state-of-the-art baseline in predicting the viral infection progression. These results highlight significant modeling performance gains obtained with PPM.

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