F<scp>ORMULA</scp>: <u>F</u>act<u>OR</u>ized <u>MU</u>lti-task <u>L</u>e<u>A</u>rning for task discovery in personalized medical models

Abstract

Medical predictive modeling is a challenging problem due to the heterogeneous nature of the patients. In order to build effective medical predictive models we need to address such heterogeneous nature during modeling and allow patients to have their own personalized models instead of using a one-size-fits-all model. However, building a personalized model for each patient is computationally expensive and the over-parametrization of the model makes it susceptible to the model overfitting problem. To address these challenges, we propose a novel approach called FactORized MUlti-task LeArning model (FORMULA), which learns the personalized model of each patient via a sparse multi-task learning method. The personalized models are assumed to share a low-rank representation, known as the base models. FORMULA is designed to simultaneously learn the base models as well as the personalized model of each patient, where the latter is a linear combination of the base models. We have performed extensive experiments to evaluate the proposed approach on a real medical data set. The proposed approach delivered superior predictive performance while the personalized models offered many useful medical insights.

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