A0526
Title: Challenges of modeling longitudinal intensive care unit data
Authors: Joel Dubin - University of Waterloo (Canada) [presenting]
Abstract: Prediction of health outcomes is an important component for determining how to make recommendations and treat individuals. Regarding treatment, the intensive care unit (ICU) is a place where many such decisions are made. A primary goal for ICU patients is treating them to achieve positive outcomes (e.g., hospital discharge alive, improvement from in-hospital ailments, extended survival). A major analytical issue is the preponderance of information available at ICU entry (e.g., age, sex, co-morbidities, prescriptions, vital signs), and especially longitudinally (e.g., vital sign changes, dynamic renal function, in-ICU treatment). We will present some interesting analytic challenges utilizing longitudinal data for predictive modeling from a large ICU database, and discuss a few remedies that we have investigated.
