IEEE INTELLIGENT SYSTEMS Published by the IEEE Computer Society Time-to-Event Predictive Modeling for Chronic Conditions Using Electronic Health Records
chronic illness and the escalating cost of chronic care, the need to facilitate clinical decision making for chronic care has never been higher. However, existing healthcare systems are oriented toward acute problems and are inadequate in managing chronic conditions. To enable effective chronic care, it is critical to be able to capture and represent a patient's disease progression pattern over time so that timely and personalized interventions can be made. Electronic health records (EHRs) are a reliable source of longitudinal observations for monitoring the progression of chronic conditions in clinical practice. Recent years have seen surging interests in EHR data analytics for clinical decision support and knowledge discovery. Although significant progress has been made to move the current practice in this direction, prognostic modeling frameworks and tools tailored for longitudinal EHR data analysis to support chronic care management remain inadequate. Time-to-event modeling (also known as survival analysis) is a statistical technique for representing and predicting the length of time to an event occurrence based on an individual's traits. 1,2 Time-to-event analysis considers not only whether an event will occur, but also the length of time to its occurrence. We use the phrase "time-to-event analysis" instead of "survival analysis" because it's more descriptive of the method and because survival isn't our focus. Indeed, caring for patients with chronic conditions involves a wide array of events other than O ne hundred and forty-one million Americans-almost half the US population-were living with one or more chronic conditions in 2010, and the patient population is expected to increase at a speed of more than 10 million new cases per decade. Given the increasing number people of living with S m a r t a n d C o n n e C t e d H e a l t
