Mining Disease Progression Patterns for Advanced Disease Surveillance

In this paper, we present a smart disease surveillance system that reveals insights into disease trajectories indicating risks of subsequent diseases starting from the current health conditions, for individual patients or populations. Using a pattern mining algorithm, we extract disease trajectory patterns from temporally modeled encounters of 17 million patients in the medical knowledge graph and develop a disease surveillance system on 477,933 mined patterns of disease progression. The system predicts future disease trajectory of individual patients and facilitates in-depth exploration into disease mechanisms, root causes and future disease progression at a patient cohort level thereby enabling early interventions for complex diseases and promoting an evidence based precision medicine approach for healthcare providers.