Multiple domain and multiple kernel outcome-weighted learning for estimating individualized treatment regimes.

ABSTRACT: Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient's own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this paper, we propose an ap

SUBMITTER: Xie S 

PROVIDER: S-EPMC10035569 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature