  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.
