Disease progression models, statistical models that assess a patient's risk of diabetes progression, are popular tools in clinical practice for prevention and management of chronic conditions. Most, if not all, models currently in use are based on gold standard clinical trial data. The relatively small sample size available from clinical trial limits these models only considering the patient's state at the time of the assessment and ignoring the trajectory, the sequence of events, that led up to the state. Recent advances in the adoption of electronic health record (EHR) systems and the large sample size they contain have paved the way to build disease progression models that can take trajectories into account, leading to increasingly accurate and personalized assessment. To address these problems, we present a novel method to observe trajectories directly. We demonstrate the effectiveness of the proposed method by studying type 2 diabetes mellitus (T2DM) trajectories. Specifically, using EHR data for a large population-based cohort, we identified a typical trajectory that most people follow, which is a sequence of diseases from hyperlipidemia (HLD) to hypertension (HTN), impaired fasting glucose (IFG), and T2DM. In addition, we also show that patients who follow different trajectories can face significantly increased or decreased risk.
- big data analytics
- data mining