A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare

TL;DR

This paper introduces a GAN-based selective ensemble method for synthesizing expressions from characteristics, addressing challenges of high-dimensional mapping and stochastic patterns, with promising results in healthcare applications.

Contribution

It proposes a novel GAN-inspired selective ensemble framework for characteristic-to-expression synthesis, handling low-to-high dimensional mapping and stochastic relationships.

Findings

Effective synthesis demonstrated in simulations

Improved stability and reduced bias in predictions

Successful application in healthcare case study

Abstract

Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for expressions using given characteristics can make great contributions to health risk management and medical decision-making. For example, predicting the resulting physiological symptoms on patients from given treatment characteristics is helpful for the disease prevention and personalized treatment strategy design. Therefore, the objective of this study is to effectively synthesize the expressions based on given characteristics. However, the mapping from characteristics to expressions is usually from a relatively low dimension space to a high dimension space, but most of the existing methods such as regression models could not effectively handle such mapping. Besides, the relationship between characteristics and expressions may contain not only deterministic patterns, but also stochastic patterns. To address these challenges, this paper proposed a novel selective ensemble characteristic-to-expression synthesis (SE-CTES) approach inspired by generative adversarial network (GAN). The novelty of the proposed method can be summarized into three aspects: (1) GAN-based architecture for deep neural networks are incorporated to learn the relatively low dimensional mapping to high dimensional mapping containing both deterministic and stochastic patterns; (2) the weights of the two mismatching errors in the GAN-based architecture are proposed to be different to reduce the learning bias in the training process; and (3) a selective ensemble learning framework is proposed to reduce the prediction bias and improve the synthesis stability. To validate the effectiveness of the proposed approach, extensive numerical simulation studies and a real-world healthcare case study were applied and the results demonstrated that the proposed method is very promising.