  Objective: We report the development of the patient-centered myAURA
application and suite of methods designed to aid epilepsy patients, caregivers,
and researchers in making decisions about care and self-management.
  Materials and Methods: myAURA rests on the federation of an unprecedented
collection of heterogeneous data resources relevant to epilepsy, such as
biomedical databases, social media, and electronic health records. A
generalizable, open-source methodology was developed to compute a multi-layer
knowledge graph linking all this heterogeneous data via the terms of a
human-centered biomedical dictionary.
  Results: The power of the approach is first exemplified in the study of the
drug-drug interaction phenomenon. Furthermore, we employ a novel network
sparsification methodology using the metric backbone of weighted graphs, which
reveals the most important edges for inference, recommendation, and
visualization, such as pharmacology factors patients discuss on social media.
The network sparsification approach also allows us to extract focused digital
cohorts from social media whose discourse is more relevant to epilepsy or other
biomedical problems. Finally, we present our patient-centered design and
pilot-testing of myAURA, including its user interface, based on focus groups
and other stakeholder input.
  Discussion: The ability to search and explore myAURA's heterogeneous data
sources via a sparsified multi-layer knowledge graph, as well as the
combination of those layers in a single map, are useful features for
integrating relevant information for epilepsy.
  Conclusion: Our stakeholder-driven, scalable approach to integrate
traditional and non-traditional data sources, enables biomedical discovery and
data-powered patient self-management in epilepsy, and is generalizable to other
chronic conditions.
