  Although recent multi-task learning methods have shown to be effective in
improving the generalization of deep neural networks, they should be used with
caution for safety-critical applications, such as clinical risk prediction.
This is because even if they achieve improved task-average performance, they
may still yield degraded performance on individual tasks, which may be critical
(e.g., prediction of mortality risk). Existing asymmetric multi-task learning
methods tackle this negative transfer problem by performing knowledge transfer
from tasks with low loss to tasks with high loss. However, using loss as a
measure of reliability is risky since it could be a result of overfitting. In
the case of time-series prediction tasks, knowledge learned for one task (e.g.,
predicting the sepsis onset) at a specific timestep may be useful for learning
another task (e.g., prediction of mortality) at a later timestep, but lack of
loss at each timestep makes it difficult to measure the reliability at each
timestep. To capture such dynamically changing asymmetric relationships between
tasks in time-series data, we propose a novel temporal asymmetric multi-task
learning model that performs knowledge transfer from certain tasks/timesteps to
relevant uncertain tasks, based on feature-level uncertainty. We validate our
model on multiple clinical risk prediction tasks against various deep learning
models for time-series prediction, which our model significantly outperforms,
without any sign of negative transfer. Further qualitative analysis of learned
knowledge graphs by clinicians shows that they are helpful in analyzing the
predictions of the model. Our final code is available at
https://github.com/anhtuan5696/TPAMTL.
