  Multi-task learning (MTL) is a machine learning technique aiming to improve
model performance by leveraging information across many tasks. It has been used
extensively on various data modalities, including electronic health record
(EHR) data. However, despite significant use on EHR data, there has been little
systematic investigation of the utility of MTL across the diverse set of
possible tasks and training schemes of interest in healthcare. In this work, we
examine MTL across a battery of tasks on EHR time-series data. We find that
while MTL does suffer from common negative transfer, we can realize significant
gains via MTL pre-training combined with single-task fine-tuning. We
demonstrate that these gains can be achieved in a task-independent manner and
offer not only minor improvements under traditional learning, but also notable
gains in a few-shot learning context, thereby suggesting this could be a
scalable vehicle to offer improved performance in important healthcare
contexts.
