r2glmm). In 2016, I was an adjunct professor of statistics at North Carolina Central University, teaching undergraduate and graduate level courses in statistics. From 2017-2021, I was an assistant professor of Biostatistics at University of Alabama at Birmingham. There, I studied machine learning, blood pressure, hypertension, cardiovascular disease, and wearable device data. What I like most about research is the programming that supports it. I am an enthusiastic R user, I’ve made a few R packages, and I’ve taught introductory courses to programming.
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Optimized to fit, interpret, and make predictions with oblique random survival forests (ORSFs), this R package is written with an intuitive, formula based API. In addition to fitting ORSFs, several methods to estimate the importance of individual variables using an ORSF are available.Read more
Risk prediction can help direct treatment and interventions to patients who are most likely to benefit. The oblique random survival forest, a machine learning algorithm for risk prediction, has been used to develop a risk prediction algorithm for heart failure and to identify specific factors for adults who are black or white that drive predicted heart failure risk, with adverse social determinants of health being a major driver for adults who are black. This talk covers these topics and also introduces methods to increase the computational efficiency and interpretability of the oblique random survival forest.Read more