Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules.

ABSTRACT: Due to heterogeneity for many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientific community. One main goal of precision medicine is to develop the most effective tailored therapy for each individual patient. To that end, one needs to incorporate individual characteristics to detect a proper individual treatment rule (ITR), by which suitable decisions on treatment assignments can be made to optimize patients' clinical outcome. For binary treatment settings, outcome weighted learning (OWL) and several of its variations have been proposed recently to estimate the ITR by optimizing the conditional expected outcome given patients' information. However, for multiple treatment scenarios, it remains unclear how to use

SUBMITTER: Zhang C 

PROVIDER: S-EPMC7731977 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature