  Complication risk profiling is a key challenge in the healthcare domain due
to the complex interaction between heterogeneous entities (e.g., visit,
disease, medication) in clinical data. With the availability of real-world
clinical data such as electronic health records and insurance claims, many deep
learning methods are proposed for complication risk profiling. However, these
existing methods face two open challenges. First, data heterogeneity relates to
those methods leveraging clinical data from a single view only while the data
can be considered from multiple views (e.g., sequence of clinical visits, set
of clinical features). Second, generalized prediction relates to most of those
methods focusing on single-task learning, whereas each complication onset is
predicted independently, leading to suboptimal models. We propose a multi-view
multi-task network (MuViTaNet) for predicting the onset of multiple
complications to tackle these issues. In particular, MuViTaNet complements
patient representation by using a multi-view encoder to effectively extract
information by considering clinical data as both sequences of clinical visits
and sets of clinical features. In addition, it leverages additional information
from both related labeled and unlabeled datasets to generate more generalized
representations by using a new multi-task learning scheme for making more
accurate predictions. The experimental results show that MuViTaNet outperforms
existing methods for profiling the development of cardiac complications in
breast cancer survivors. Furthermore, thanks to its multi-view multi-task
architecture, MuViTaNet also provides an effective mechanism for interpreting
its predictions in multiple perspectives, thereby helping clinicians discover
the underlying mechanism triggering the onset and for making better clinical
treatments in real-world scenarios.
