virtual patient

Wednesday, March 7, 2018 - 11:30am - 12:30pm
Adam Himes (Medtronic)
Medical device manufacturers are increasingly using predictive computer models, also called virtual patient models, that simulate clinical outcomes. In some cases, these virtual patient models can be incorporated into a study in a way that is analogous to how some Bayesian clinical trials incorporate historical data as prior information. Benefits of this approach may include increased information from the clinical study, more confidence in the clinical outcome, and in some cases smaller or shorter duration studies.
Wednesday, March 7, 2018 - 10:00am - 11:00am
Drew Pruett (University of Mississippi)
Human physiology is a complex system composed of many interacting negative feedback loops involving hormones, nerves, transporters, physical anatomy and other factors. Redundancy in physiology complicates accurate prediction of a patient’s response. For any given medical therapy or intervention, 10-90% of potential patients are resistant, achieving less than half of the expected response. Prediction of nonresponse is a necessary step in minimizing inefficiencies in health care and biomedical product evaluation and regulation.
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