  Existing patient data analytics platforms fail to incorporate information
that has context, is personal, and topical to patients. For a recommendation
system to give a suitable response to a query or to derive meaningful insights
from patient data, it should consider personal information about the patient's
health history, including but not limited to their preferences, locations, and
life choices that are currently applicable to them. In this review paper, we
critique existing literature in this space and also discuss the various
research challenges that come with designing, building, and operationalizing a
personal health knowledge graph (PHKG) for patients.
