Personal Health Knowledge Graphs for Patients
This review addresses the problem of improving personalized healthcare analytics for patients, but it is incremental as it focuses on critiquing and discussing challenges rather than presenting new solutions.
The paper critiques existing patient data analytics platforms for lacking personal context and discusses the challenges of designing, building, and operationalizing personal health knowledge graphs (PHKGs) to incorporate patient-specific information like health history and preferences for better recommendations and insights.
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.