Journal Article

Deep doubly robust outcome weighted learning

Kosorok, Michael R., Jiang, Xiaotong, Zhou, Xin

2024

Overview

Precision medicine is a framework that adapts treatment strategies to a patient’s individual characteristics and provides helpful clinical decision support. Existing research has been extended to various situations but high-dimensional data have not yet been fully incorporated into the paradigm. We propose a new precision medicine approach called deep doubly robust outcome weighted learning (DDROWL) that can handle big and complex data. This is a machine learning tool that directly estimates the optimal decision rule and achieves the best of three worlds: deep learning, double robustness, and residual weighted learning. Two architectures have been implemented in the proposed method, a fully-connected feedforward neural network and the Deep Kernel Learning model, a Gaussian process with deep learning-filtered inputs. We compare and discuss the performance and limitation of different methods through a range of simulations. Using longitudinal and brain imaging data from patients with Alzheimer’s disease, we demonstrate the application of the proposed method in real-world clinical practice. With the implementation of deep learning, the proposed method can expand the influence of precision medicine to high-dimensional abundant data with greater flexibility and computational power.

• Publisher: Springer US,Springer Nature B.V
• Subject: Artificial Intelligence / Artificial neural networks / Computer Science / Control / Deep learning / Gaussian process / Machine Learning / Mechatronics / Natural Language Processing (NLP) / Precision medicine / Robotics / Robustness / Simulation and Modeling