Colloquium_2020-10-20: Machine Learning Improves Estimates of Environmental Exposures-edited
From Richard Ressler
Speaker: Yuri Levin-Schwartz of the Icahn School of Medicine at Mount Sinai
Tuesday October 20, 2020 2:30 PM
Abstract: In environmental studies, the true level of exposure to environmental chemicals (e.g., lead, mercury, etc.) is unknown and must be estimated. The most common way to estimate exposure is with the use of "exposure biomarkers," measures of the chemical of the interest or metabolite, in a biological sample (e.g., blood, urine) from the subject. The inherent assumption is that higher levels of exposure should generally translate into higher levels in the exposure biomarkers. However, different biomarkers have variable utility as surrogate measures of exposure and no single biomarker is ideal for all chemicals. A natural question is: can multiple biomarkers be combined to improve exposure estimates? In this talk, I will describe the use of multiple machine learning methods to address this question and show how their successful application can improve our ability to highlight the effects of environmental exposures on human health.
Introduction by Dr. Aleka Kapatou, Department of Mathematics and Statistics, AU