Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options.

ABSTRACT: To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the largest benefit can be considered. Recently, a machine learning approach, O-learning, has been proposed to estimate an optimal individualized treatment rule (ITR), but it is developed to make binary decisions and thus limited to compare two treatments. When many treatment options are available, existing methods need to be adapted by transforming a multiple treatment selection problem into multiple binary treatment selections, for example, via one-vs-one or one-vs-all comparisons. However, combining multiple binary treatment selection rules into a single decision rule requires careful consideration, because it is known in the multicategory lea

SUBMITTER: Zhou X 

PROVIDER: S-EPMC6437674 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature