Multi-Task Learning and Its Applications to
Biomedical Informatics
Dr. Jiayu Zhou
Samsung Research America
November 13, 2014
1:30 pm
Civil Engineering Room 007
In many fields one needs to build predictive models for a set of related machine learning tasks.
Traditionally these tasks are treated independently and the inference is done separately for each
task, which ignores inherent connections among the tasks. Multi-task learning aims to improve the
generalization performance by building models for all tasks simultaneously, leveraging inherent
relatedness of these tasks. In this talk, we show how multi-task learning can be applied to improve
the predictive modeling from electronic medical records (EMR). We consider a novel data-driven
framework for densifying EMR to address the challenges from the data sparsity when EMR are
used for predictive modeling. By treating the densification of each patient as a learning task, the
proposed multi-task learning algorithm simultaneously densifies all patients. As such, the
densification of one patient leverages useful information from other patients. Experiments on real
clinical data show that the densification can significantly improve the predictive performance.