  A broad spectrum of data from different modalities are generated in the
healthcare domain every day, including scalar data (e.g., clinical measures
collected at hospitals), tensor data (e.g., neuroimages analyzed by research
institutes), graph data (e.g., brain connectivity networks), and sequence data
(e.g., digital footprints recorded on smart sensors). Capability for modeling
information from these heterogeneous data sources is potentially transformative
for investigating disease mechanisms and for informing therapeutic
interventions.
  Our works in this thesis attempt to facilitate healthcare applications in the
setting of broad learning which focuses on fusing heterogeneous data sources
for a variety of synergistic knowledge discovery and machine learning tasks. We
are generally interested in computer-aided diagnosis, precision medicine, and
mobile health by creating accurate user profiles which include important
biomarkers, brain connectivity patterns, and latent representations. In
particular, our works involve four different data mining problems with
application to the healthcare domain: multi-view feature selection, subgraph
pattern mining, brain network embedding, and multi-view sequence prediction.
