Making Machine Learning Fast and Interpretable
Methods and Application to the Prediction of Incident Heart Failure
By Byron C Jaeger in Seminar
September 7, 2022
Abstract
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.
Date
September 7, 2022
Time
12:00 AM
Location
Winston-Salem, NC
Event
Wake Forest University School of Medicine Grand Rounds, September 2022
This talk will be given in the Wake Forest Biotech Place auditorium on September 8 at noon eastern time.