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Some prediction problems in intensive care units (ICUs)

Thursday, March 3, 2016 - 16:00
Joel A. Dubin, Associate Professor, Department of Statistics & Actuarial Science, and School of Public Health & Health Systems, University of Waterloo
Statistics Seminar

A primary goal for ICU patients is treating them to achieve positive patient 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 longitudinally (e.g., vital sign changes, dynamic renal function, in-ICU treatment). I will present a number of interesting analytic challenges in predictive modeling that my collaborators and I have encountered from a large ICU database, and discuss a few remedies that we have investigated, including implementation of a patient similarity step in an effort to improve predictive accuracy.