Towards longitudinal analysis of a population's electronic health records using factor graphs

In this feasibility study, we demonstrate the use of a factorgraph- based probabilistic graphical model approach to process longitudinal data derived from a population's electronic health records (EHR). Processing of EHR allows for forecasting patient-specific health complications and inference of population-level statistics on several epidemiological factors. As a case-study, we provide preliminary results and demonstrate feasibility of our approach by processing the EHR of a diabetic cohort in Singapore. Our model passes the feasibility test as we are able to forecast a series of health complications of a new patient based on the factor functions inferred from EHR of 100 diabetic patients spanning 10-years. This forecast gives both the caregivers and the patient a better view of the patient's health in the coming years and increases patient's motivation to stay healthy and conform to medication plan. Furthermore, our approach informs commonly occurring health complications in the population that warrant hospital readmissions, which helps a physician/clinician in decide when to intervene to avoid complications in order to improve the patient's quality of life and minimize the cost of care.