Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy

Abstract

Machine-learning techniques can produce accurate disease predictability better that traditional statistical regression. These tools may prove clinically useful for the automated prediction of patients who develop early biochemical recurrence after robot-assisted prostatectomy. For these patients, appropriate individualized treatment options can improve outcomes and quality of life.