Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection.

ABSTRACT: Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. One AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) has been developed. However, GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional g

SUBMITTER: Ge Q 

PROVIDER: S-EPMC7759680 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature