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


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.


September 7, 2022


12:00 AM


Winston-Salem, NC


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.

Posted on:
September 7, 2022
1 minute read, 19 words
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