An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests.

ABSTRACT: With new treatments and novel technology available, precision medicine has become a key topic in the new era of healthcare. Traditional statistical methods for precision medicine focus on subgroup discovery through identifying interactions between a few markers and treatment regimes. However, given the large scale and high dimensionality of modern data sets, it is difficult to detect the interactions between treatment and high dimensional covariates. Recently, novel approaches have emerged that seek to directly estimate individualized treatment rules (ITR) via maximizing the expected clinical reward by using, for example, support vector machines (SVM) or decision trees. The latter enjoys great popularity in clinical practice due to its interpretability. In this paper, we propose a new rewa

SUBMITTER: Doubleday K 

PROVIDER: S-EPMC7286561 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature