Abstract Number:
3314
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
yang sui (1), Yang Bai (2)
Institutions:
(1) N/A, N/A, (2) Shanghai University of Finance and Ecnomics, N/A
Co-Author:
Yang Bai
Shanghai University of Finance and Ecnomics
First Author:
Presenting Author:
Abstract Text:
Precision medicine aims to uncover the optimal personalized treatment plan, offering thoughtful decision support based on the characteristics of each patient. With the rapid advancement of medical imaging technology, integrating patient-specific imaging features into individualized treatment rules has become critical. We introduce a novel, data-driven approach that leverages imaging data and other variables to guide the selection of the best treatment options. Specifically, this study considers tensor covariates and scalar covariates within a regression framework and proposes an estimation method based on Tucker decomposition. This method effectively reduces the number of parameters, leading to efficient estimation and feasible computation. For handling high-dimensional tensors, we further employ sparse Tucker decomposition to reduce the parameter number. Additionally, we developed an alternating updating algorithm that incorporates an Alternating Direction Method of Multipliers. Under certain conditions, we show the asymptotic properties of these estimators. The numerical performance of our method is validated through simulations and high-order medical imaging data applications.
Keywords:
individualized treatment rules|tensor regression|imaging data| | |
Sponsors:
Biometrics Section
Tracks:
Personalized/Precision Medicine
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