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Thursday, April 11 • 3:50pm - 4:20pm
Machine Learning as a Tool to Address HIV Health Disparities

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HIV infection is a world-wide epidemic with almost 20 million people infected and tens of millions of deaths since the early 1980’s.  Due to better healthcare access and treatment, HIV morbidity and mortality in the US isn’t as dire for some subpopulations and has declined ~ 19% between 2005-2016. Unfortunately, there are specific demographic groups in the US which carry the burden of the disease far more than others. African Americans, especially those living in the South, lag behind other regions of the US in key HIV prevention and care indicators resulting in disparities in HIV incidence and prevalence. With the knowledge these disparities are not adequately accounted for through high-risk sexual behavior like the frequency of unprotected intercourse or the number of sexual partners, new tools are desperately needed to assist in addressing the disparities in HIV incidence /prevalence in minority communities.
This talk proposes to cover the following: 1) Highlight recent applications of machine learning in studies on HIV, 2) Discuss the downsides of ML healthcare, and 3) Present the results of a novel HIV health disparities study, applying machine learning/logistic regression techniques.

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avatar for Kimberly Deas

Kimberly Deas

Health Data Scientist, Rutgers University
Kimberly is a Health Data Scientist Consultant who works with for and non-profit organizations, academic institutions, and government agencies, providing statistical and data analytical support to their programs. Formerly educated in Chemistry and Pharmacology at the University of... Read More →


Thursday April 11, 2019 3:50pm - 4:20pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA