Risk controlled decision trees and random forests for precision Medicine
Abstract
Statistical methods generating individualized treatment rules (ITRs) often focus on maximizing expected benefit, but these rules may expose patients to excess risk. For instance, aggressive treatment of type 2 diabetes (T2D) with insulin therapies may result in an ITR which controls blood glucose levels but increases rates of hypoglycemia, diminishing the appeal of the ITR. This work proposes two methods to identify risk-controlled ITRs (rcITR),...
Paper Details
Title
Risk controlled decision trees and random forests for precision Medicine
Published Date
Nov 16, 2021
Journal
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