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Wednesday, November 9, 2022 – 12:00PM to 1:00PM
Juliessa M. Pavon, MD, MHS, Associate Professor of Medicine (Geriatrics) and Head & Neck Surgery and Communication Sciences; Senior Fellow in the Center for the Study of Aging and Human Development; Durham VA Health System GRECC with host Mary Solomon, BS, MS, Duke AI Health Fellow
Artificial intelligence algorithms can complement traditional screening methods and result in a population-based strategy for identifying high risk patients within large health systems - including the use case of identifying patients with poor physical function and at risk for early palliative care needs. In this 1-hour seminar, we'll discuss (1) using electronic health record (EHR) data for a data-driven approach to categorize patients with poor functional status, (2) applying unsupervised machine learning algorithms for clustering visualization of the data, (3) training and testing a supervised machine learning algorithm to distinguish between patient functional status types, (4) learning features of EHR clinical data that predict functional status types, and (5) evaluation of the model for potential health inequities. Our session will benefit attendees by increasing their understanding of current applications of AI using health system EHR data, as well as the initials steps for piloting implementation of the model for population-based screening.